{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: Feature Selector Usage\n",
    "\n",
    "In this notebook we will walk-through using the `FeatureSelector` class for selecting features to remove from a dataset. This class has five methods for finding features to remove: \n",
    "\n",
    "1. Find columns with a missing fraction greater than a specified threshold\n",
    "2. Find features with only a single unique value\n",
    "3. Find collinear features as identified by a correlation coefficient greater than a specified value\n",
    "4. Find features with 0.0 importance from a gradient boosting machine\n",
    "5. Find features that do not contribute to a specified cumulative feature importance from the gradient boosting machine\n",
    "\n",
    "The FeatureSelector is a work in progress! Any contributions on GitHub are appreciated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Dataset\n",
    "\n",
    "This dataset was used as part of the [Home Credit Default Risk competition on Kaggle](https://www.kaggle.com/c/home-credit-default-risk/). It is intended for a supervised machine learning classification task where the objective is to predict if a client will default on a loan. The entire dataset can be downloaded [here] and we will work with a small sample of 10,000 rows. \n",
    "\n",
    "The feature selector was designed to be used for machine learning tasks, but can be applied to any dataset.\\ The feature importance based methods do require a supervised machine learning problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>247408</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>172512.0</td>\n",
       "      <td>13477.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153916</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>229065</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>463500.0</td>\n",
       "      <td>20547.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282013</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>549882.0</td>\n",
       "      <td>17739.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142266</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>518562.0</td>\n",
       "      <td>20695.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      247408       0         Cash loans           F            Y   \n",
       "1      153916       0    Revolving loans           F            Y   \n",
       "2      229065       0         Cash loans           F            N   \n",
       "3      282013       0         Cash loans           F            N   \n",
       "4      142266       0         Cash loans           F            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               N             2          108000.0    172512.0      13477.5   \n",
       "1               Y             2          135000.0    180000.0       9000.0   \n",
       "2               Y             0          112500.0    463500.0      20547.0   \n",
       "3               Y             0          135000.0    549882.0      17739.0   \n",
       "4               Y             0           90000.0    518562.0      20695.5   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        0.0   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         1.0   \n",
       "3                       0.0                         0.0   \n",
       "4                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        0.0                        1.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         7.0  \n",
       "3                         1.0  \n",
       "4                         1.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('data/credit_example.csv')\n",
    "train_labels = train['TARGET']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several categorical columns in the dataset. The `FeatureSelector` handles these using one-hot encoding when using the feature importance based methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns = ['TARGET'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation\n",
    "\n",
    "The `FeatureSelector` has five functions for identifying columns to remove:\n",
    "\n",
    "* `identify_missing`\n",
    "* `identify_single_unique`\n",
    "* `identify_collinear`\n",
    "* `identify_zero_importance` \n",
    "* `identify_low_importance`\n",
    "\n",
    "These methods find the columns according to specified criteria. The identified features are stored as attributes in the `FeatureSelector`. We can remove the identified features manually or use other `FeatureSelector` methods for removal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Instance\n",
    "\n",
    "The `FeatureSelector` only requires a dataset with observations in the rows and features in the columns (standard structured data). We are working with a classifified machine learning problem so we also pass in training labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Missing Values\n",
    "\n",
    "The first feature selection method is straightforward: find any columns with a missing fraction greater than a specified threshold. For this example we will use a threhold of 0.6 which corresponds to finding features with more than 60% missing values. (This method does not one-hot encode the features first)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_missing(missing_threshold=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features identified for removal can be accessed through the `removal_ops` dictionary of the `FeatureSelector` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OWN_CAR_AGE',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_features = fs.removal_ops['missing']\n",
    "missing_features[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot a histogram of the missing column fraction for all columns in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f390f628d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For detailed information on the missing fractions, we can access the `missing_stats` attribute, a dataframe of the missing fractions for all features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <td>0.6751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <td>0.5075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>0.5531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>0.5546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_MEDI</th>\n",
       "      <td>0.5126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>0.5059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>0.1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <td>0.5059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    missing_fraction\n",
       "AMT_CREDIT                    0.0000\n",
       "FLOORSMIN_MODE                0.6751\n",
       "HOUSETYPE_MODE                0.5075\n",
       "EXT_SOURCE_1                  0.5531\n",
       "NONLIVINGAREA_MEDI            0.5546\n",
       "APARTMENTS_MEDI               0.5126\n",
       "AMT_INCOME_TOTAL              0.0000\n",
       "LIVINGAREA_MEDI               0.5059\n",
       "EXT_SOURCE_3                  0.1948\n",
       "LIVINGAREA_MODE               0.5059"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.missing_stats.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Single Unique Value\n",
    "\n",
    "The next method is straightforward: find any features that have only a single unique value. (This does not one-hot encode the features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 features with a single unique value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_single_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLAG_MOBIL', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_17']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_unique = fs.removal_ops['single_unique']\n",
    "single_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot a histogram of the number of unique values in each feature of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f391d0f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can access a dataframe with the number of unique values per feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FLAG_DOCUMENT_12</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nunique\n",
       "FLAG_DOCUMENT_12                 1\n",
       "NAME_HOUSING_TYPE                6\n",
       "FLOORSMAX_MODE                  24\n",
       "FLOORSMIN_AVG                   78\n",
       "AMT_REQ_CREDIT_BUREAU_QRT        8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.unique_stats.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Collinear (highly correlated) Features\n",
    "\n",
    "This method finds pairs of collinear features based on the Pearson correlation coefficient. For each pair above the specified threshold (in terms of absolute value), it identifies one of the variables to be removed. We need to pass in a `correlation_threshold`. \n",
    "\n",
    "This method is based on code found at https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/\n",
    "\n",
    "For each pair, the feature that will be removed is the one that comes last in terms of the column ordering in the dataframe. (This method does not one-hot encode the data beforehand unless `one_hot=True`. Therefore correlations are only calculated between numeric columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 features with a correlation greater than 0.97.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.975)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AMT_GOODS_PRICE',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlated_features = fs.removal_ops['collinear']\n",
    "correlated_features[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view a heatmap of the correlations above the threhold. The features which will be dropped are on the x-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U7ozXmTNnbrleBYnDH9kOLi4uhIWFkZaWxldffQXA/PnzLeU3K1juypUrt9wn7hW32q8h/9EQt1qPm597V61aNebMmcPZs2fZu3cv48ePxzAMXnnlFRYsWPCHx1iyZMk/vd8WvP+/tZ+IiNxJSuZE5G8rMjKS4sWLM2vWLM6ePXvLtgWzVgEBAXh4eLBr1y67t4DftGkT8Nt3ffw9mjRpwvnz5y2nnf0RP//8MwCdOnWyqfv1Le8LFCtW7LYfewBYTg/cuHGjTV1GRga7d+/G09PztmdBb8VkMlGnTh1eeeUV1q9fD8DKlSt/c7mCU1WffvppoqKibF4Fd060d0rr3r177c6AFswqFTxwvOC937x5s01SZRiGZXv/eh/p3bs3kJ/EHTlyhG3bthEUFISvr69VuyZNmgD/O93SWT322GOYTCarU2lvV/HixXn00UcZMWKEJYm7nfe/MA0aNCAzM5OdO3fa1N3uZ7rg/bf3efrll1/0eAIRcQglcyLyt1WrVi1GjBjBuXPnePrpp+3ObmVlZTF58mTLNT1ubm707NmTc+fOER8fb9X2yy+/5IsvvqBWrVqWWb87oeCW6H379rX78PJTp07xww8/3LIPf39/AJtnd23atIkPP/zQ7jI+Pj4cP378tsfZvHlzatasyRdffGEzsxUfH8+5c+fo2bPnH75l/pEjR+w+061gxsPT0/OWy1+9epVFixZRokQJFi1axOzZs21eixcvpnz58ixfvtxmW1+8eJF33nnHqmzu3Lns37+ftm3bWk7Tq1q1Ko8//rjlUQQ3+/jjj/n++++t2hcouDZu6dKlfPjhhxiGYXOKJUD//v1xcXEhOjraboJw6dIly3V79zI/Pz969OjBtm3bePfdd+3OJu7YscPynybfffcdv/zyi02b233/byUiIgKAmJgYcnNzLeUnTpxg8uTJuLi42JyW+2stWrSgevXqfP7551afM8MweO21137Xf4yIiNwuXTMnIn9r48aNIysriylTpvDggw/Stm1b6tWrh6urK0eOHOHLL7/k/PnzjBs3zrLM+PHj2bRpE+PGjWPbtm00adLE8pw5Ly8vyzVZd0r79u0ZM2YMb775JrVq1aJ9+/b4+/tz/vx5Dh06xNdff824ceOsnnP2ax07dqRatWpMmDCB7777jnr16vHjjz/y+eef8+yzz7JkyRKbZdq2bcuiRYsICQmhQYMGFC9enODgYLs3s4D8mbzExESCgoJ45pln6N69O/7+/uzYsYOvvvqKmjVr2iRDv8e+ffvo0qULjz32GPXq1cPPz48TJ06wfPlyihcvbrmGrjALFy7k2rVrPP/889x3331227i4uNC7d28mT57M/PnzGTRokKWuZcuWvPfeeyQnJ/PYY4/x008/sWzZMkqVKsX06dOt+vnggw9o0aIFL774IqtWraJu3bocOHCAlStX4uvrywcffGA3fp8+fRg9ejQTJ06kZMmSdmdS69Wrx4wZM3j55Zd58MEHeeaZZ6hZsyZXrlzh8OHDbNq0icjISBISEn5rk951M2bM4Mcff2TEiBHMmzePZs2aUapUKY4dO8Y333zDwYMHSUtLw8vLiy+//JJXX32V5s2bExAQQNmyZTl8+DArV67E09OTAQMG/OFx9OnTh6VLl7JixQoefvhhOnToYHnO3Pnz55k0aRI1atS4ZR/FihVj1qxZPPPMMzz55JOW58x99dVXpKWl8fDDD/Pf//73D49RRMSuIrprpojIPW3Xrl1G3759jVq1ahmenp6Gu7u7Ua1aNaNnz57Gv//9b5v2Z8+eNQYOHGj4+/sbrq6uRrly5YyQkBBj//79Nm0LboV+5MgRu7ELHk2wYcOGW45x/fr1RseOHQ1fX1/D1dXV8PPzM5o1a2a8+eabVre5v9Vz5rp162b4+voaXl5exmOPPWYsXLiw0PZpaWlGjx49jHLlyhnFihUzAOOTTz65ZQzDyH+EQEhIiFGuXDnD1dXV8Pf3NwYOHGicPXvWpq2/v7/h7+9vd31bt25tdSv3Y8eOGaNGjTKaNm1qlC9f3nBzczOqVq1qhISE2L2l/K81bdq00Fv632z//v0GYNSvX98wjP/ddj8iIsL473//a7Rv397w9vY27rvvPiM4ONj47rvv7PaTmppqPP/888b9999vuLi4GPfff7/x/PPP2zz64NfLmEwmAzCef/75W45z586dRlhYmFGxYkXLPtiwYUNj1KhRxg8//PAbW8PW7T6aICIiwm49dh5lcav3t0BGRoYxYcIEo1GjRkaJEiUMT09Po3r16sazzz5rzJ0718jNzTUMwzAOHDhgDBo0yGjQoIFRtmxZw93d3ahRo4YRGRlpHDhwwKrPW33mCvu85ebmGhMnTjTq169vuLu7G97e3kbr1q2NFStW2PRxq/1/8+bNRqtWrQxPT0/Dx8fH6N69u/HLL7/Y7M8iIneCyTDuoaukRURERERE5LbomjkREREREREnpGRORERERETECSmZExERERERcUJK5kRERERERJyQkjkREREREREnpGRORERERETECSmZExERERERcUJK5kRERERERJyQy90egPw95J485dD+XSv6AXBm0nSHxin/6gDOjJ/q0BgA5UcO5uyU9x0ex3fIK6Sv3+DwON5PPc7BVs84NEbtzWsAyNz3nUPjeD5SD4DDnXo6NE6NlQu4+Olih8YAKNOrO4eDezg8To3Vi0gb85bD49z/5uukxYx1bIz4WAB+ee5Fh8bx/9eHAFxZ+x+HxinZ/gnOTJzm0BgA5YdFO3xdoGjXJz093aExvL29gaL5bgOKZH2K6riWNnqcw+PcP240OalHHR7HrVpVfundz6Ex/OfPcmj/4jiamRMREREREXFCSuZERERERESckJI5ERERERERJ6RkTkRERERExAkpmRMREREREXFCSuZERERERESckJI5ERERERERJ6Rk7jbt3LmTatWqkZ2dTWRkJC+//DJdu3Zl/fr1ACxatIhmzZpZ2gcHBzNgwAA6d+7Mvn37mDVrFmazmVq1amE2m/nss88ICAggKSkJgIkTJxIUFARgaWM2mzl58iRPPPEEU6ZMAWDUqFHs378fs9lMYGAgYWFhDB48mI0bN9KjRw/69+/P7NmzbcYfGRnJsGHDANi1axceHh4A7N27lz59+hAdHU3//v3JysoiMTGRTp06MWjQIPr27cuRI0dITU2lQYMGlnFlZ2c7bmOLiIiIiMhv0kPDb9O8efMYPXo0y5cvB2DKlClcuHCB+Ph4nnrqKVatWkW3bt3YsWMHTZo0wdvbm+nTp7N9+3Y2bNjA4MGDAQgLCyMhIQGApUuXsmHDBkJCQjh8+DBlypQBIDAw0NIGwNfXl927d3Pp0iUg/6GbCQkJxMXFERYWRkBAAIMGDWLcuHE88MADha7DtWvXyM7OZvHixTz55JMAjBs3jgULFuDm5samTZuYMWMGPj4+9O/fn/bt23P+/HmGDh3K2LFjCQoK4p133rnldsrOzrZJ9Nzd3fW/BiIiIiIid5j+xr4NmZmZXLx4kYiICFauXAnA8OHDGTNmDP379+f48eP4+PgQHh7O3LlzAUhPT+eVV17hpZdeIiwszG6/JpOJihUrkpSURNOmTS3lu3fvtsyAZWZmAjBy5EjGjx9f6BhHjhzJzJkziYqKYsWKFXbbPPvssyxYsIDr169z3333AeDm5oabmxsAjRs35vvvv7dapmzZsuTk5ACwbt06zGYzQ4cOLXQc8fHxlCpVyuoVHx9faHsREREREfljNDN3G5KSkjh9+jTR0dHs37+fcuXKsWbNGsupim+++SbHjh3jjTfeYOvWrWRkZODt7c3777/PypUrWbNmDX379rXbd58+fWjVqhUHDhxgzZo1gO3MHMDDDz/MBx98wOXLl+32U7FiRSZNmgRAhw4d6Ny5s02b1q1b06JFC6ZOncr06dMByM3NJTc3F1dXV3bv3k3dunWtlrlw4YIl2budmbmYmBibZM/d3R3OX7zlciIiIiIi8vsombsNy5YtY9WqVXh4eLBz506rWTTDMEhOTmb16tVA/rVzBdfBAXTq1Ilu3brx3HPP5Sc1v1K1alUOHjyIq6urpaxgZg7yZwALvP7669SpU4e3337bpp/Zs2ezd+9ebty4QcuWLQtdl+3bt+Pq6mpJ5mJiYnjhhRcoU6YM2dnZTJ48mc8++4wZM2bwxRdfcPXqVeLi4oD8mbmCUz3feecdSpcubdO/u7u73fXMLXREIiIiIiLyRyiZuw1Lly61/Ny4cWNu3Lhh+d1kMlkSOYAePXoAEB4ebilbsmSJ5eeFCxfa/FyQyBX8fujQIav4BeWVK1cmPT3dUl6QZAG88MILt1yHxMREu302atSIOXPmWNVFRkYSGRlp08fevXtvGUNERERERIqOkrm/qISEBE6dOgXkn4LZr1+/uzwiERERERG5k5TM/UUVnKYpIiIiIiJ/TbqbpYiIiIiIiBNSMiciIiIiIuKElMyJiIiIiIg4ISVzIiIiIiIiTshkGIZxtwchIiIiIiIiv4/uZilF4ubn4zmCt7c3ADm/HHNoHDf/KmT/fMShMQDca1Yn7/RZh8dxqeDr8PcG8t+fnNSjDo3hVq0qUIT72tHjDo3jVrUyWT/86NAYAB51HiT74M8Oj+NeuyZ5Z885PI6Lbzlyj59waAzXypUAyD2R5tg4le4H4ELipw6N4xPZq8iOa45eF8hfH0d/PiH/M1pUxxtHf3ZcfMsBRXP8zEr5yaExADwCHiiy483BFkEOj1N7y7oiO96I89FpliIiIiIiIk5IyZyIiIiIiIgTUjInIiIiIiLihJTMiYiIiIiIOCElcyIiIiIiIk5IyZyIiIiIiIgTUjInIiIiIiLihJTMiYiIiIiIOKEiTebCw8NJS0sjKysLX19fmjVrhtlsZtiwYZY2TZs2JSkpCYCNGzfy+OOPExUVxdChQy1lPXr0oH///syePdsmRmRkJC+//DIRERF89NFHANSqVQuz2YzZbObkyZPs27ePyMhIBgwYgNlsJiUlhcTERNauXUtiYiKNGjXixo0bpKSkEBcXx8aNG2nVqpVlrDk5OXTv3p3MzEy+/PJLxo8fbzXWV199ldTUVEaNGmUZV0H/gKVfgLS0NHx9fUlLS+PkyZOYzWYef/xxgoODMZvNXLt2jZ07d1KtWjWys7PZv38/ZrOZwMBAwsLCGDx4MHFxcaSkpHDhwgWioqIYMGAAERERHD58mNTUVLy9vTlxIv8humFhYXbfm0WLFtGsWTMAjhw5wsCBAy11AwYMIDU1lUWLFln6f/nll8nLy7PpJzs7mytXrli9srOzb28HERERERGR2+ZSlMEmTJhATEwM999/PzExMXh5eWE2my31ycnJhISEsGrVKkJCQgAIDQ3FbDYTGhoKwLJlyxg3bhwPPPBAoXGmTJkCwPPPP09UVBSBgYEkJCRY6gcPHsz8+fNxc3MDIC8vj+TkZEt9/fr1mT9/Po0bN7aU9erVy2qsr732GkOHDuXcuXMsXLiQr7/+2jLWrl273vY2mTNnDpMmTWLu3LmMHDmShIQEEhMT8fPzo3379gDMmzeP0aNHs3z5ckJDQ0lISCAuLo6wsDACAgIsiWF8fDzDhw8nICCAy5cvEx0dzT//+U86dOjAW2+9xYwZMwodx6pVq+jWrRs7duygSZMmlqQb4PTp03h7e7Nu3TpLgnz9+nWKFy9u0098fDxjx461KouNjeXVV1+97W0iIiIiIiK/rUhn5vz8/GjZsiXXr1+nYcOGfPrpp5jNZt566y0A5s6dS58+ffDx8eH48eMALF68mKCgIB566CEARo4cycyZM4mKimLFihV24wwfPpyQkBC6dOkCwO7duy0zc5mZmbi4uODm5kZycjJ9+/Zl1apVVsuHhoaycuVKSzID2Iy1QYMG7N27l86dO1uSmsWLF2M2mwkODr7tbbJr1y7Cw8P55ptv7NZnZmZy8eJFIiIiWLly5S37On78OAEBAQCUKlWK3NxcAPz9/SlZsiT79+8vdDkfHx/Cw8OZO3cuAF26dGHZsmUsXbqUrl278vPPP1O/fn0A5s+fT2hoKL/88otNXzExMVy+fNmvRQtNAAAgAElEQVTqFRMTc3sbQ0REREREbluRzswB1KhRg5ycHMB6tisjI4PNmzcTGxvL2bNnSUxMpEWLFnTv3p0XX3yRPn36kJOTQ8WKFZk0aRIAHTp0oHPnzjYx3n33XTw8POjRowc9evSwmZnLy8sjJyeHpk2bkpWVRUpKCh4eHpZ6k8lEdHQ07733HlWrVrUZK8DMmTMZNGgQSUlJPPvsswB0797d0iY1NdVqTGXKlOHs2bMAnDlzBh8fH77++mtOnDiB2Wzm2LFjbNmyhRYtWlgtl5SUxOnTp4mOjmb//v0cPXrUMqZfq1SpEj/99BMPPPAAV65cwdXV1VI3atQoBgwYYHe5Tz75hGPHjvHGG2+wdetWMjIy6Nq1K+Hh4RiGwbx587h27RozZ84EoHfv3hw6dIjMzEybvtzd3XF3d7cpL3jPRURERETkzijyZO5mn376Kd9++y3FixencePGjB492nJNV6dOnWjevDkAxYsX57nnnuPDDz/E3d2dvXv3cuPGDVq2bGm33yFDhnDjxg3LTFLBzBzkz9q9/vrr9OvXj5IlS5KZmcmLL77IgQMHrPpo3bo1U6dOtSRON4912LBh7Ny5k48++oh69eoxZswYu0nlunXruHTpEgBTp07FbDbzzTffcO7cOaZOncqoUaNYsmQJlSpV4uTJk8TFxdkkc8uWLWPVqlV4eHiwc+dOEhMTeeONN+yu96hRoxg5ciTe3t5cunTJql3p0qVp2LAhq1evtlrGMAySk5Mt5YsWLSIpKYnw8HAqVKgAgIeHBx4eHrRr146oqCi8vb25evUq5cuXtzsOERERERFxPJNhGMbdHoT89aWnpzu0f29vbwByfjnm0Dhu/lXI/vmIQ2MAuNesTt7psw6P41LB1+HvDeS/PzmpRx0aw61a/n+8FNm+dvS4Q+O4Va1M1g8/OjQGgEedB8k++LPD47jXrkne2XMOj+PiW47c4yccGsO1ciUAck+kOTZOpfsBuJD4qUPj+ET2KrLjmqPXBfLXx9GfT8j/jBbV8cbRnx0X33JA0Rw/s1J+cmgMAI+AB4rseHOwRZDD49Tesq7IjjfifO7qzNydkJCQwKlTpwCoWLEi/fr1u8sjuvctXLiQlJQUALy8vBgxYsRdHpGIiIiIiPxeTp/M3Xwdm9yewh5PICIiIiIizkMPDRcREREREXFCSuZERERERESckJI5ERERERERJ6S7WYqIiIiIiDghzcyJiIiIiIg4Iae/m6U4h9xTpx3av6tf/gPOz0yc5tA45YdFOzxGQZwL8z5zeByfPqGk//srh8fxbteWn9t3c2iMmmuXAHBt+06HxinRrDEAR7r2cWic6kvncWHOAofGAPCJ6MnBVs84PE7tzWs4M2m6w+OUf3UAp96c4NAYfmPyH+dy7MWBDo1T5cP3ALjy+TqHxinZIajIjmuOXhfIX5+zU953eBzfIa8U2XPmLi5Y4tA4ZXrmH5+LYn2K6rvt7LSZDo/jG/1SkT2n8+jz/R0ao+onMxzavziOZuZERERERESckJI5ERERERERJ6RkTkRERERExAkpmRMREREREXFCSuZERERERESckJI5ERERERERJ6RkTkRERERExAkpmRMREREREXFCf/lkLjExkbVr1wIQGRnJtWvX6Nmzp6V+woQJbN68mbCwMABq1qzJsmXLACxl+/btIzIykgEDBmA2m0lJSQFg586dVKtWjezsbEv/L7/8Ml27dmX9+vUALFq0iGbNmlniBQcHM2DAADp37sy+ffuYNWsWZrOZWrVqYTab+eyzzwgICCApKQmAiRMnEhQUBGBpYzabOXnyJE888QRTpkwBYNSoUezfvx+z2UxgYCBhYWEMHjyYjRs30qNHD/r378/s2bPtbqN3333Xsk02bdrExIkTLXVhYWFcu3aNadOm0a9fPwYMGMDIkSML3d7Z2dlcuXLF6lWwfURERERE5M5xudsDKGrFixenZMmSnDx5kooVK7JlyxZGjBjBjBn5T75v2LAh8+bNo2PHjpZl3nrrLebPn4+bmxsAeXl5AMybN4/Ro0ezfPlyQkNDAZgyZQoXLlwgPj6ep556ilWrVtGtWzd27NhBkyZN8Pb2Zvr06Wzfvp0NGzYwePBgID9pSkhIAGDp0qVs2LCBkJAQDh8+TJkyZQAIDAy0tAHw9fVl9+7dXLp0CQBvb28SEhKIi4sjLCyMgIAABg0axLhx43jggQcK3SZ79+6lUqVKnDx5ktatWzNp0iSGDRvGyZMnKV26NEeOHOH06dPMmjXLav3tiY+PZ+zYsVZlsbGxvG5++bfeGhERERER+R3+8jNz9kRERDBnzhy2bdtmNWsG+cleVFSUJXEBcHFxwc3NjeTkZPr27cuqVavIzMzk4sWLREREsHLlSkvb4cOHM2bMGPr378/x48fx8fEhPDycuXPnApCens4rr7zCSy+9ZJn5+zWTyUTFihVJSkqiadOmlvLdu3dbZuYyMzMBGDlyJOPHjy90XUeOHMnMmTOJiopixYoVNvXbtm2jYcOG9OrVizlz5gDQtGlTtm/fTmJiIhERERw4cIDAwEAA3nvvPbp06UJOTo7deDExMVy+fNnqFRMTU+j4RERERETkj/nbzcwB/OMf/2D8+PGkpqYSGxtrUx8cHExYWBjp6elA/kxUTk4OTZs2JSsri5SUFJKSkjh9+jTR0dHs37+fo0ePAvmnLHp4eADw5ptvcuzYMd544w22bt1KRkYG3t7evP/++6xcuZI1a9bQt29fu2Ps06cPrVq14sCBA6xZswawnZkDePjhh/nggw+4fPmy3X4qVqzIpEmTAOjQoQOdO3e2qk9MTCQjI4NDhw6xZ88eYmJiiIiI4M033yQtLY3XXnsNb29vFi5cyLPPPsvAgQPZs2cPN27csBvP3d0dd3d3m/Jcu61FREREROSP+lskc9OmTWP58uUsXbrUkgy1aNGCLVu2ULFiRbvLDB8+nCZNmgDw+uuv069fP0qWLElmZiYvvvgi77zzDqtWrcLDw4OdO3eSmJhotbxhGCQnJ7N69Wog/9q5guvgADp16kS3bt147rnn7CY/VatW5eDBg7i6ulrKCmbmCsZX4PXXX6dOnTq8/fbbNv3Mnj2bvXv3cuPGDVq2bGlVd+3aNS5evMjixYuB/10/2KpVK9LS0mjRogUA9erVo0KFCvTr1w8vLy/c3d2txiUiIiIiIkXvL5/MRUZGEhkZCWA1qzV8+HCrhGjhwoVW/zZq1Mhybdgjjzxik6wtXbrU8nPjxo1p3LixVb3JZLIkcgA9evQAIDw83FK2ZMkSm/g3/1yQMBX8fujQIasYBeWVK1e2zCICxMXFWX5+4YUXKEyJEiUsiRzAiBEjLD//+pTM6OjoQvsREREREZGi95dP5sRaQkICp06dAvJPwezXr99dHpGIiIiIiPwRSub+ZgpO0xQREREREef2t7ybpYiIiIiIiLNTMiciIiIiIuKElMyJiIiIiIg4IZNhGMbdHoSIiIiIiIj8ProBihSJmx+d4Aje3t4AZKX85NA4HgEPkHsizaExAFwr3U/uqdOOj+NXgYzdex0exyuwAZnf7ndoDM9H6wNwsEWQQ+PU3rIOgJzDqQ6N41ajGnlnzzk0BoCLbzmubd3h8Dglmjch9+Qph8dxrejn8M+oa6X7Acg9fsKxcSpXyo/j4GOBq18Fh68L5K9PUR3Xsn889NsN/yT3B2sV2Xebo48FLr7lgKL5rnZ0jII4eafPOjyOSwXfIlufnKPHHRrDrWplh/YvjqPTLEVERERERJyQkjkREREREREnpGRORERERETECSmZExERERERcUJK5kRERERERJyQkjkREREREREnpGRORERERETECSmZExERERERcUJO8dDwxMREpk2bxq5du/jpp59YuHAhWVlZpKenc/XqVXr27En79u2pWbMmEydOpEuXLoSFhbFw4ULLvwXCwsJ4+umnKVGiBCEhIQB06tSJIUOG8OOPPxIQEECfPn1ISUmhePHimM1mS/z9+/fj5uZGiRIlGD9+PABDhgzBZDIxefJkAB544AHatm3LuXPnmDNnDiVKlCA0NJTHHnuMYcOGkZqaSrdu3WjUqBFZWVl8+OGHDBo0iJMnT3Lp0iXq1q3LP/7xD2JiYjh8+DDu7u40atSI6OhoABYvXkyVKlXw9/enZ8+e1K9fn5SUFCpVqkRYWBht27Zlz549fPnllzz55JM8/vjj7N+/n5MnT+Lq6srgwYOpU6eOzTZu2rQpw4YNIyQkhBdffJE333wTPz8/0tLSiI2NZfz48YwaNQo3Nzfy8vJo3749nTt3tuknOzub7OxsqzJ3d/c7syOIiIiIiIiF08zM1a9fn/nz5wPw+eefU716dd5//33mzJnD7NmzycvLo2HDhsybN4+8vLxb9tW9e3cWL14MQHJyMo0bN8ZkMlnqn3jiCUtyBvDdd99x+vRpZs2axfTp03nrrbcAyMrK4vLly1y6dImsrCwAGjZsSEJCAg0bNiQlJYWTJ09SpUoV9uzZY+nvqaeeYtasWXh5eZGRkUFCQgJDhw6lV69eJCQkULVqVVq0aMGKFSvYsWMHtWvXtiwbHR1NQkICMTExAHTo0MEyHoB+/fqRkJBAYGAgCQkJhIaGsmfPHmbPns3MmTPtJnLJycmEhISwatUqAMLDw5kzZw4Ac+bMITw8nPj4eIYNG8a0adP44IMPCA4Otrtt4+PjKVWqlNUrPj7+lu+HiIiIiIj8fk6TzIWGhrJy5UqysrI4cOAATZo0sdTVrFmTtLQ0ihcvTlRUFLNmzbplX15eXvj4+HDixAnmzJlDZGSkVX3Tpk05cuQIp0+fBuDAgQMEBgYC8N5779GlSxdycnJYunQpzzzzDB06dGDZsmUA7N27l+joaC5dusSjjz5KYmIiYWFhBAYGsn37dgD+85//0K1bN3JzcylTpozdMbZp04ZNmzaRlJRE9+7dLeXTpk3DbDaTkJAAgL+/PyVLlmT//v2Fru/AgQN5+eWXMZvNnDx50qZ+7ty59OnTBx8fH44fP07Lli3ZunUrhmGwbds2WrRowfHjx6lduzbnzp1j8ODBjB071m6smJgYLl++bPUqSDxFREREROTOcZpkzmQyER0dzXvvvUedOnXYtWuXpe7w4cP4+fkBEBwczObNm0lPT79lfxEREcyYMYOLFy9SuXJlm/rXXnuNt99+G4C6deuye/duID8xKlu2LDdu3GDBggV88cUXrF69mgULFgDQoEEDpk2bxoQJEyhevDjLli1j9uzZ7N27l8TERCB/5m/JkiUUK1aMc+fOFbq+lSpVIi8vjxIlSljKC2bmzGazpWzUqFGW0z7tad++PTNnziQyMpJPPvnEqi4jI4PNmzcTGxvL0aNHLWP8xz/+wYQJE2jevDkAlStX5uDBg5QrV46pU6dy8OBBu7Hc3d0pWbKk1UunWYqIiIiI3HlOcc1cgdatWzN16lQ6dOjAwYMHGThwIFevXuX555/H1dXV0m748OGWmbsrV65YEp+IiAhLm6ZNm/LSSy8xevRou7Fq1aqFl5cX2dnZ1KtXjwoVKtCvXz+8vLxwd3fnxIkTVKlShRkzZgAwYMAAUlNTrfrYtGkT3bp1Y9SoUUD+9XrXrl2z1A8aNIh333230ERs1KhRGIbB+vXrLWXTpk1j+fLllC9fnr59+wJQunRpGjZsyOrVq+32ExUVRYkSJThz5gwjR460qlu8eDGjR48mLCwMyL9+0DAMIiIiqF27tiVpi4mJYeTIkbi5uVGsWDHatWtnN5aIiIiIiBQNk2EYxt0ehPz1/dZM6Z/l7e0NQFbKTw6N4xHwALkn0hwaA8C10v3knjrt+Dh+FcjYvdfhcbwCG5D5beGnAt8Jno/WB+BgiyCHxqm9ZR0AOYdTHRrHrUY18s7an7m/k1x8y3Ft6w6HxynRvAm5J085PI5rRT+Hf0ZdK90PQO7xE46NU7lSfhwHHwtc/So4fF0gf32K6riW/eMhh8dxf7BWkX23OfpY4OJbDiia72pHxyiIk3f6rMPjuFTwLbL1yTl63KEx3KranqUmzsGpZubkzzt16pTlejuAZ555hsaNG9/FEYmIiIiIyB+hZO5vxs/Pj7i4uLs9DBERERER+ZOc5gYoIiIiIiIi8j9K5kRERERERJyQkjkREREREREnpLtZioiIiIiIOCHNzImIiIiIiDgh3c1SisSZ9AyH9l/e2wv4az0jR3F+fwwoumcaal+7d+P8lZ7JVVRx/mr7QFHFaRM33aExNsYNALSvKU7RfYeK89HMnIiIiIiIiBNSMiciIiIiIuKElMyJiIiIiIg4ISVzIiIiIiIiTkjJnIiIiIiIiBNSMiciIiIiIuKElMyJiIiIiIg4ISVzIiIiIiIiTkgPDXeAxMREli5dSvXq1UlPT2fMmDFUrFiRBx98kKSkJAIDAwkPD2f8+PGUKVOGPn36MHHiRIYPH07ZsmXx9vZmwoQJNn0uXryYKlWq4O/vT8+ePalXrx6HDh3Cz8+Pbt26Ub9+fdq0acPYsWOpUaMGpUuXJjo6moSEBN555x0AsrOzGTJkCMWKFePKlSsMHDiQI0eOcP78ecxmM+PGjaNVq1YMHz6cBg0aAPDKK68wadIkvLy8cHFxwTAMJk6ciLu7u826Z2dnk52dbVVmr52IiIiIiPw5SuYcpH///rRv357z588zdOhQnn76acaMGcO8efMIDAxkwoQJxMTEcP/99xMTE8OePXt48skn6devX6F9RkdH0759ewBSU1N55plnmD9/Pr169cLT09PSLjQ0FLPZTNeuXW36+PDDD+ncuTNBQUHk5eURGhrKkiVLMJvNLF++nGvXrtGqVSuqV69OQkKC1bKTJ0/Gw8ODf//738ycOZOBAwfa9B8fH8/YsWOtymJjY+n/6ojftf1EREREROTWdJqlg5UtW5acnByWL19Onz59uHjxIpmZmfj5+dGyZUuuX79Ow4YNCQ4O5ty5c/Tr188yi/Zr06ZNw2w2W5KsGjVqcPjwYebNm0fv3r0t7RYvXozZbCY4ONimjwMHDtCkSRMAXFxc8PDwwDAM4uPjeeONN4iNjQXgyJEjmM1mzGYzx48ft+rjscceIyUlxe4YY2JiuHz5stUrJibm9284ERERERG5Jc3MOdiFCxc4deoU586dY+DAgZw6dYolS5bQu3dvatSoQU5ODgBubm689tprAJjNZk6cOEGlSpWs+vr1zBxAs2bNWLFiBZ07dyY5ORmA7t27YzabrdoVqFOnDrt27eKpp54iLy+PrKwsTCYTZcqUoW7dunh4eADYnZkrsHv3bgICAuzWubu72z+tMifjN7aUiIiIiIj8HkrmHGTGjBl88cUXXL16lTZt2hAUFETTpk3Jysqid+/eVjNpAFu2bGHevHl4eHiQk5ODn5+fTZ/Tpk1j+fLllC9fnr59+wLQu3dvevbsyaFDhwody7p167h06RIAU6dOZejQoXz++edcvnyZkSNH2l2mYGYOsMQaOnQoLi4umEwmm2v6RERERESkaJkMwzDu9iDkr+9MumNn5sp7ewGQnp7u0Dje3t4Oj6E4fzwGFM0+UFRx/irvTVHHyTt7zqExXHzLAdrXFMebNnHTHRpjY9wAQPua4hTdd6g4H83M3aPWrl1rOW0S4LXXXsPNze0ujkhERERERO4lSubuUe3bt7dcHyciIiIiIvJrupuliIiIiIiIE1IyJyIiIiIi4oSUzImIiIiIiDgh3c1SRERERETECekGKFIkiuy2yus3ODbOU49zbftOh8YAKNGsMQdbBDk8Tu0t68j64UeHx/Go8yBnJk5zaIzyw6IByD112qFxXP0qAHBmwnsOjVN+xEAy93/v0BgAnvUf4kLipw6P4xPZizOTHHsbd4Dyrw7g3PsfOjRGuVdeBODivxY5NE6Z53oAkPV9ikPjeDwUUGTHNUevC+Svj6O/CyD/+6CovtsOtnrGoXFqb14DFM13dVEd185O/cDhcXwHv0ze6bMOj+NSwZcL8z5zaAyfPqEO7V8cR6dZioiIiIiIOCElcyIiIiIiIk5IyZyIiIiIiIgTUjInIiIiIiLihJTMiYiIiIiIOCElcyIiIiIiIk5IyZyIiIiIiIgTUjInIiIiIiLihJTMOVhiYiJr1661/P7uu+/Ss2dPy+81a9Zk2bJlAISFhQEQHBxMdHQ0/fv3Z/To0Za2ixYtolmzZpbfg4ODGTBgAJ07d2bfvn0A1KpVC7PZjNls5uTJkwAMGTKEoUOHFjrGm/s9cuQIAwcOtNQNGDCA1NRUFi1aRFRUFAMGDODll18mLy/Pbl/Z2dlcuXLF6pWdnX17G0tERERERG6bkrkitnfvXipVqmRJtBo2bMi8efOskiNvb2+mTZvGjBkzqFq1KitXrgRg1apVdOvWjR07dljaTZ8+nVGjRrFhwwYAAgMDSUhIICEhgYoVK5KVlcXly5e5dOkSWVlZdsd0c7/Vq1cnLS2NrKwssrKyOH36NN7e3qxbt46PPvqI6dOnM336dFxcXOz2FR8fT6lSpaxe8fHxd2z7iYiIiIhIPiVzRWjbtm00bNiQXr16MWfOHACKFy9OVFQUs2bNsrvMY489RkpKCsePH8fHx4fw8HDmzp0LQHp6Oq+88govvfSSZVZv9+7dlpm5zMxMli5dyjPPPEOHDh0sM4A3s9dvly5dWLZsGUuXLqVr1678/PPP1K9fH4D58+cTGhrKL7/8Yne8MTExXL582eoVExPz5zaciIiIiIjYsD+9Ig6RmJhIRkYGhw4dYs+ePZYkJzg4mLCwMNLT022W2b17NwEBAXzyySccO3aMN954g61bt5KRkYG3tzfvv/8+K1euZM2aNfTt29cyM1dgwYIFlC9fHoCzZ89aneIJ2O23a9euhIeHYxgG8+bN49q1a8ycOROA3r17c+jQITIzM+2uo7u7O+7u7jblOTk5f2yjiYiIiIiIXUrmisC0adNYsmQJiYmJ5ObmAjBhwgQ2b95saTN8+HCaNGkC5M+4RUdHc+PGDXx8fHjhhReYOXMmq1evBvKvcUtKSrIs26lTJ7p168Zzzz1nmZkr6LNKlSrMmDED+N/1b9WqVQPAMAySk5Nt+g0PD6dChQoAeHh44OHhQbt27YiKisLb25urV69aEkQREREREUc62CLojvRTe8u6O9LPvUTJnINFRkYSGRkJwIcffmgpHzFiBACtWrUCoFGjRpbr5gqSq5vdXNajRw8AwsPDLWVLliwB4NChQ1bLFSRyANOnT7eqM5lMdvuF/AT0ZqGhoYSGhtpdRxERERERKXpK5v5mFi5cSEpKCgBeXl6WpFJERERE5J5k0m0+CqNk7m+m4EYpIiIiIiJOwWS62yO4ZymZExERERGRe5apmJK5wmjOUkRERERExAlpZk5ERERERO5dumauUErmRERERETk3qVr5gplMgzDuNuDEBERERERsedQ2053pJ9aX628I/3cSzQzJ0Ui9/gJh/bvWrkSABc/XezQOGV6db9jD668ldpb1nFt+06HxynRrDHXtu5wfJzmTTj99mSHxqjw2lAAck+kOTSOa6X7ATj1z/EOjeP3xkiODxzp0BgAld8bz5mJ03674Z9Ufli0w48DkH8sSP/3Vw6N4d2uLQBpr7/p0Dj3vzUGgLPTZjo0jm/0Sxxs+bRDYwDU/voLh68L5K/Ppc+WOjxO6dCupKenOzSGt7c3ABk7v3FoHK/GjQC4kJHl0Dg+Xh5FdlwrquNN9sGfHR7HvXZN0mLGOjTG/fGxDu3/T9MNUAqlZE5ERERERO5ZJp1mWSglcyIiIiIicu8qphugFEZbRkRERERExAlpZk5ERERERO5dOs2yUErmRERERETk3qVkrlA6zVJERERERMQJaWZORERERETuWSbdAKVQSuZEREREROTepWSuUErm/oDExEQWL15MlSpV2LlzJ59++ikBAQEA7N27l8mTJ1O6dGmuX7/O5MmTMZlMDBkyhGLFinHlyhUGDhxIYGAgDzzwAE888QRnzpxh7ty5pKenM3DgQMqWLYu3tzcTJkwgICCAcePGERISwsSJE1m/fj3r1q0DoGnTpgwbNoyQkBAOHTrEpEmT+OCDD4iNjaVdu3Y0b97catweHh5s2bKFwMBAXn31Vc6fP09iYiKTJk3ip59+4vr167Rs2ZKIiAiCg4OpUaMG169fx8fHh3HjxhEXF0dKSgqlS5emYcOG9OvXz2bbZGdnk52dbVXm7u6u83lFRERERO4wJXN/UHR0NO3btycuLs6qfNy4cSxYsAA3Nzc2bdrEjBkzcHNzo3PnzgQFBZGXl0doaChLliyhYcOGfPDBB8THx3Pw4EGOHDnCk08+aZUkPfLII2zYsIGQkBAOHz5MmTJlAEhOTiYkJIRVq1YREhJCrVq1qFevHmPGjCE3N9cmkQN48sknWbRoEfXq1SMzMxOA/fv3c/78eWbOnAnASy+9RFBQEN7e3kybNg2AWbNmsXLlSgDi4uL+n707j6/pzv84/rqJLEVspbYKaml0GEVKlNKWYjCWsSS0kUwRVyqWVEQICaVBbW00Ddpp0BJiG9RQnRlb1VjbUcZIW6k1lpYIkYjI74/8cseVe1Xae9JI38/H4zx6873nfD/fc3J700+/myVxtSUmJoYpU6ZYlUVFRTFxyNBCPmEREREREbQAyn2ow+Rnio2NxWw2c+TIEatyV1dXXF1dAWjZsiVHjx7l2LFjtGrVCoBSpUrh7u5Obm4uhw8fZtCgQezatYsmTZrQrVs3Ll++TFBQEDNmzADydryvUaMGq1evxsfHxxJn6dKl+Pv7U6lSJc6cOQOAn58f8+bN47XXXrPZ5rJly3L79m1WrFhBr169APjPf/5Dy5YtLec8/fTTJCcnW133zDPPcPz4cSAvmTObzSQlJRI09U8AACAASURBVNmMERERQVpamtURERHxYA9VREREROQeJpPJIUdJpGTuZwoJCSE+Pp4mTZpYlWdnZ5OdnQ3AgQMHeOqpp2jUqBH79+8H4Pbt22RmZmIymWjWrBlLly6ladOmJCcn4+rqyoQJE1i0aBEpKSmcPXsWAH9/f8twSoCMjAx27txJVFQUp06dIiEhAYDx48ezYcMGoqKi7La7d+/evPvuu7Rr1w6ARo0aceDAAcv7X331FfXr17e65sCBA5beuOjoaOLj4+nXr5/N+t3c3ChXrpzV4ebm9kDPVERERESkACeTY44SSMMsHSA6OpoKFSrQunVrIiIiGDJkCBUrViQrK8syZy40NJRNmzaRlpZGeHi41fWhoaGEhYUxZMgQli1bhru7O7du3aJatWoAeHp6kpycjIuLCwBJSUlERkbi5+cHQI8ePUhKSqJ58+a8+OKLJCcns2rVKvr371+grc899xxffPEFOTk5ADRp0oQtW7YQHBxMTk4OPj4+VK9enfT0dEJCQrhz5w6VKlVi6NChHDp0yHKvjRo1YtSoUUY+VhERERERuQ8lcz9DYGCg5fW9c+YAlixZUqAsLi6uQFliYiIAVapUsfSutW3b1uY5+Ylc/s93y5/Plm/YsGE22313XS4uLpaYYWFhBc795JNPCpTZulcREREREUOZNJjQHiVzJVRqairx8fGWn7t27Wo1N05ERERE5KFQQodIOoKSuRKqWrVq6kkTERERESnBlMyJiIiIiEixVVJXonQEJXMiIiIiIlJ8ac6cXUrmRERERESk+NKcObtMubm5ub92I0RERERERGxJ8XvVIfXUSfyLQ+opTtQzJyIiIiIixZbJScMs7VEyJ0UiPT3d0Po9PDwAuLZpq6FxynXvzK1TZwyNAeDq+TgXZ8caHuexsSFkHj1ueBz333lxftJ0Q2NUf2MiANnnUg2N41KjGkCR3M/tS5cNjQFQqkplLs6cb3icx8JHk9y2s+FxGuzeyvnIaYbGqD4tEoAry5MMjVNxYD8Abn1/2tA4rrVrFdn3mtH3Ann3k7Zxi+Fxyv+xS5H9bbs4Z4GhcR57fQRQNH+ri+p7LbldV8PjNNi5ucjup6i+b4otLYBil9JcERERERGRe8TFxVG3bl3c3d1p0aIFu3btuu/58+fP58knn+SRRx6hVq1ajBkzhszMTEPbqJ45EREREREpvn6FnrmVK1cyevRo4uLiaNOmDQsXLuQPf/gDx44dw9PTs8D5H3/8MePHj+cvf/kLzz77LCdOnCAwMBCAefPmGdZO9cyJiIiIiEjx5eTkmKMQ5s6dy+DBgxkyZAiNGjVi/vz51KpVi/fee8/m+V988QVt2rRh4MCB1KlTh06dOjFgwAAOHDjgiCdgl5I5EREREREp8bKysrh27ZrVkZWVVeC8W7ducfDgQTp16mRV3qlTJ/bs2WOz7rZt23Lw4EH27dsHwHfffcfmzZvp1q2b42/kLkrmRERERESk2DKZTA45YmJiKF++vNURExNTIN7ly5fJycmhatWqVuVVq1YlNdX2Qmt+fn688cYbtG3bFhcXF+rVq8cLL7zA+PHjDXkm+TRnTkREREREii8HbRoeERFBaGioVZmbm5vd8033zNXLzc0tUJZv+/btTJ8+nbi4OFq1asU333zDqFGjqF69OpMmTfrljbdDyZyIiIiIiBRfJscMJnRzc7tv8pavcuXKODs7F+iFu3jxYoHeunyTJk3C39+fIUOGANCkSRNu3LhBUFAQEydOxMmgvfKUzDlYQkICSUlJ1KpVi9q1a/Pmm29y/PhxatasiZ+fHy+++CKHDh3is88+o2PHjrzwwgvMmzeP5s2bc+bMGd577z1q1qxJVlYWTz75JKtXr8bb25uEhARiY2PZv38/J06cIDExkejoaCZMmMDVq1e5c+cOXbt25dChQxw/fpwKFSrQvHlzevTowciRI3n00Ufx8PBg1qxZBdq8b98++vfvz3//+19u377NkCFDWLFiBQCzZs3Cx8eHnJwcli5dSpkyZcjOzmbatGlUqVKlqB+viIiIiIihXF1dadGiBdu2baN3796W8m3bttGzZ0+b12RkZBRI2JydncnNzSU3N9ewtiqZM0BISAhdunQhJSWFf//735YuV4CgoCAgb1xtfHw8AOvWrSMuLo4VK1awf/9+atasybp165g0aRLLli3D29sbyMvwP/roI1q2bAnApk2bqFu3LkOHDgXyJmseOnSI6OhovLy8LHV37NjREteWZcuWERkZyfr16/H19aVcuXKcO3eOGjVqsHv3bsLCwujTpw9r1661XJOTk2OzrqysrAITSR/k/4CIiIiIiNj0K2xNEBoair+/P97e3rRu3ZpFixZx6tQpzGYzAIMGDaJmzZqWOXd//OMfmTt3Ls2aNbMMs5w0aRI9evTA2dnZsHZqARQDxMbGYjab2bJlC7Vr16ZcuXIcOXLE7vnff/89Q4cOJTY2lo4dOwKwfv16/P39uXLlCjdv3gTA19eXDRs2WDYfPHbsGM8884ylHldXVwCio6Mxm80kJSXRrVs3Ll++TFBQEDNmzCgQ++bNm1y5coWAgAA2bNgAQEBAAEuWLGHPnj20bt2aS5cuWfbT2Lx5My+//DL79++3eS8POrFURERERORBmJxMDjkKw9fXl/nz5zN16lSefvppdu7cyebNm6lduzYAp06d4vz585bzIyMjef3114mMjOSpp55i8ODBdO7cmYULFzr0WdxLPXMGuLtnLj4+nvHjxzNixAi759euXZvFixcTGxvLv/71Lxo0aMDRo0cZOXIkqamprFmzBsibhBkSEsI777yDp6cnjRo14uDBgzz99NNAXs8cYNUzBzBhwgQAzGYzZ8+epWbNmpb3Vq9ezYULFwgJCeHIkSOcOnWKZ599lpkzZ5KSkkJUVBRVqlTh9OnTAHTt2pWLFy9y9epVm/dib2JpfttERERERB4GwcHBBAcH23xv+/btVj+XKlWKqKgooqKiiqBld8Ut0mi/EbGxsaxfv56MjAxq1Khhmb/2ySef3Pe6YcOG4efnR9OmTVm8eDE+Pj5kZmbyyiuv0L17dwDat2/P/Pnz8fT0pHv37kRERFg+ZF26dAHykrkKFSrQqFEjWrRowbJly3B3d+fWrVtUq1bNKua6devYuHEj7u7u7Nu3j4SEBCZPnkzbtm3ZvXs3NWrUAGDEiBEEBgZSvnx5bty4QUREhM17sDexVMmciIiIiPwsv8Iwy4eFkjkHCwwMJDAwsEB5aGioVY9VYmJigdeurq5W89IA3N3dWb16tVXZunXrLK/vHTrZo0ePArHbtm1rt713x2vZsqVlPl5YWBhhYWGW91544QVeeOEFu/WIiIiIiBjCoJUgSwIlc78xW7ZsYe/evZafJ0yYYJlrJyIiIiJS3JiUzNmlZO43pkuXLpbhmCIiIiIi8vBSMiciIiIiIsWX5szZpWRORERERESKLyVzdmkAqoiIiIiIyENIPXMiIiIiIlJ8aQEUu0y5ubm5v3YjREREREREbDk7arxD6qn59oyfPukho545KRK3L102tP5SVSoDcH37bkPjlH2+LTd27/3pE3+hMm19yPzPfw2P497oSZLbdjY8ToPdW0n/9B+GxvDo9CIAGQcOGxqntHczAK5t+buhccp16UDm0eOGxgBw/52X4fcC/38/RfSZvr5rj6Exyj73LADXd3xubJz2bQDISv7W0DhuDeoV2fea0fcCefdj9N8CyPt7kJ6ebmgMDw8PADKPnzA0jrtXQ4AiuZ+i+l4z+plB3nMz+plB3nMrqu8befgomRMRERERkeJLC6DYpWRORERERESKLyclc/YomRMRERERkeLLpAVQ7NGTEREREREReQipZ05ERERERIotk4ZZ2qVkTkREREREii/tM2eXnoyIiIiIiMhDSD1zIiIiIiJSfGlrArvUM3ePhIQEWrRowZ07dzh+/DjR0dGMHz+e1157jYCAALZs2QJAvXr1WLduHQB+fn5W/8zn5+fHkiVLWL16taWsR48e/POf/yQ+Pp7t27dTq1Ytbty4QWZmJoGBgQDExsYSFBTEiBEjCA8Pt1w7ZswYQkNDLT83bNiQ4cOH06dPH27cuMH27dtp164dZrOZsWPHWs7z8fGxasO97q7XVntzc3OZPHkywcHBvPbaa7z11lt268rKyuLatWtWR1ZWlt3zRURERETux2QyOeQoiZTM2dCkSRM++ugjADZt2kTdunV59913WbJkCe+//z63b9+mefPmLFu2jNu3b9+3rn79+pGUlATA3r17admypdWHqUOHDsydO9fy89dff82FCxdYtGgRCxYsYPr06QBkZmaSlpbG1atXyczMBKB58+a89957eHt7k5ycDMDAgQOJj49n9uzZlph9+/Zl48aNNtt3b7222rt582Zq1apFXFwc7777LmPGjLF7vzExMZQvX97qiImJue8zEhERERGxy8nJMUcJVDLv6hfy9fVlw4YNZGZmcuzYMVq1amV5r169epw/fx5nZ2cGDx7MokWL7ltX6dKlqVSpEmfPnmXJkiWW3rd8Pj4+nDx5kgsXLgBw7NgxvL29AXjnnXfo3bs3t27dYu3atXTt2pXu3btbegQPHz7MoEGD2LVrF02aNAFg+fLlmM1mSxK4dOlS/P39qVSpEmfOnCnQvnvrtdXeo0eP8swzzwAQHR2Nv7+/3fuNiIggLS3N6oiIiLjvMxIRERERkcJTMmeDyWQiJCSEd955h0aNGrF//37Le9999x3VqlUDoFu3buzcuZP09PT71hcQEEBcXBxXrlzh8ccfL/D+hAkTePPNNwF46qmnOHDgAAAjR47k0Ucf5c6dO6xYsYK//e1vfPLJJ6xYsQKAZs2asXTpUpo2bVqgZ27ixIlkZGSwc+dOoqKiOHXqFAkJCQVi26r33vb+7ne/szyD6OhocnNz7d6rm5sb5cqVszrc3Nzu+3xEREREROwymRxzlEBaAMWO9u3bM3/+fLp3705ycjIjR47k+vXr/PnPf8bFxcVyXlhYmKXn7tq1a5jNZiAvIcrn4+PDsGHDiIyMtBmrfv36lC5dmqysLBo3bkzVqlUJCgqidOnSuLm5cfbsWcswR4ARI0aQkpJiuT40NJSwsDACAwNZvnw5X375Jc7OzrRs2ZLIyEjLXL78+W/5wzxTUlJs1ntve7t168bkyZMZPnw4bm5u1K5d2xGPWERERETkp5XQRMwRTLn362YRcZDbly4bWn+pKpUBuL59t6Fxyj7flhu79xoaA6BMWx8y//Nfw+O4N3qS5LadDY/TYPdW0j/9h6ExPDq9CEDGgcOGxint3QyAa1v+bmiccl06kHn0uKExANx/52X4vcD/308Rfaav79pjaIyyzz0LwPUdnxsbp30bALKSvzU0jluDekX2vWb0vUDe/Rj9twDy/h781MicX8rDwwOAzOMnDI3j7tUQoEjup6i+14x+ZpD33Ix+ZpD33Irq+6a4Oj9pukPqqf7GRIfUU5yoZ+43ZsaMGZYFVJ566in69+//K7dIRERERMQ+UwldvMQRlMz9xowfP/7XboKIiIiIyIPTMEu7lOaKiIiIiIg8hNQzJyIiIiIixZeTeubsUTInIiIiIiLFl4ZZ2qXVLEVEREREpNi6MH22Q+qpOnGsQ+opTjRnTkRERERE5CGkYZZSJIpqL54fMzINjVOptLvhMfLjFNXeNSUlTv5noCTFKarfjT7ThY8BRfN9A0XzWStpn4GScj9F/b1m9L6jDXZvLTHfA0Udp6i+b4otk/qf7FEyJyIiIiIixZcWQLFLaa6IiIiIiMhDSD1zIiIiIiJSbJm0mqVdSuZERERERKT40pw5u5TMiYiIiIhI8aU5c3YpzRUREREREXkIqWdORERERESKL82Zs0vJnIiIiIiIFFsmDbO066FK5rKyshgzZgxOTk5cu3aNkSNHMnDgQDp16sTZs2d5++23SU9PJzo6mipVqlC7dm3Cw8Ot6tiwYQObN2/m9OnTTJ48mVatWjFx4kTS09MpW7Ysb775ZoG48+fP59ixYzg7O/PMM8/g5OREbGws+/fv58SJEyQmJnL9+nXOnz/PsWPHaNWqFcOGDaNZs2aWOhITE9m4cSPly5fnpZdeonfv3owfP5709HSuX7/OgAED6NKlC5999hnLli2jbNmylC1bluHDhxMfH8+MGTPIysriySefZPXq1Xh7e5OQkEC1atXo0qWLzedlq93VqlUjNTWVtWvXUrFiRZ566inCw8NJSUlhypQplClThszMTGbOnMlrr71GYmIiACkpKfTu3ZtWrVoB8Pbbb+Pm5mbzd5SVlWVVZus8ERERERH5ZR6qZG7x4sX07NmTzp07c/v2bXx9fWnevDkLFizggw8+4N///jfJyckMHTqUTp062ayjR48e9OjRg8OHD7Nnzx5q1KjB7du3eeeddwgLC+P06dPUqlXLcv7XX3/N5cuXWbRoEQC3bt1i+fLlNGnShI8++oiWLVsCMHv2bFJSUiyJ173Wrl3L+++/T7ly5QDYtGkTdevWZdiwYQD07duXDh06EBcXx9q1ay2xzp07Z6lj3bp1TJo0iWXLluHt7X3fZ2Wv3fmCg4Pp0qULvr6+AEyZMoU5c+ZQqVIlcnNzycnJKVBn586dbd7b3WJiYpgyZYpVWVRUFK+//vp9rxMRERERsUmrWdr1UD2Z/F4vgFKlSuHu7s7hw4cZNWoUH3/8MT4+PgwePJgdO3YwdOhQ3n//fZv1zJ07l6FDh/LCCy9w5swZS/Lm6enJmTNnCsS8O3FydXUFwNfXlw0bNpCZmflAbX/jjTeYOHEigwcP5vPPP7e6F4B69epx7tw5PD09C8TKt379evz9/bly5Qo3b968bzx77c63cOFCWrduTdeuXQHIzMykUqVKQN5eHqVKFczzt27ditlsJjQ01G7ciIgI0tLSrI6IiIj7tlVERERExC6TyTFHCfRQJXONGjVi//79ANy+fZvMzEyaNWvG22+/zZw5cyw9X9OnT2fx4sX89a9/JTc3t0A9oaGh/O1vf2PevHnUrFnTksCdPn2axx9/vEDMgwcPWn6+desWkJfwhISE8M477zxQ25988kliY2OJj49n9uzZVvcC8N1331GtWjVOnTpVIBbAqVOnOHr0KCNHjiQ1NZU1a9b85LOy1e58w4YN47PPPmPbtm0AuLu7c+XKFQByc3O5fft2gTo7d+5MfHw8c+fOtRvXzc2NcuXKWR0aZikiIiIiP5uTyTFHCfRQDbMcOnQooaGhbNq0ibS0NMLDw3nllVcICQnh4sWLvP7666xfv56tW7fi7OxMw4YNC+wY/8EHH3D48GGuXr1KUFAQnp6eODk5ERoaipubm9UQS4AmTZrw6aefMmzYMEqVKkWLFi1wcsrLgdu3b8/8+fOtetPsmTlzJqdPn+bmzZv07NmT7t27Ex4ezsiRI7l+/Tp//vOfcXNzY9iwYQQEBFC2bFnKlStnGYb54YcfsnjxYnx8fMjMzOSVV16he/fuxMbGsn79eho2bGjVY3a/ducrU6YMrVu35pNPPmHSpEmMGTOGsmXLcuvWLWJiYjh58iRmsxnIS4C3bt3K1atXAZgxYwYVKlQo5G9QREREREQcxZRrq+tKxMHS09MNrd/DwwOAHzMebNjrz1WptLvhMfLjGP3MIO+5lZQ4+Z+BkhSnqH43+kwXPgYUzfcNFM1nraR9BkrK/RT191py286Gxmmwe2uJ+R4o6jhF9X1TXF1+d7FD6qn82lCH1FOcPFQ9cz9HYmIix48fB6B06dKMGzfuvucfP37csoIjwMCBA2nYsGGh48bHx5OamgpAjRo1CAoKKnQdheGodouIiIiIFCtaAMWuEp/M+fn5Fep8Ly8voqOjf3Hc/OGJRcVR7RYRERERkYdDiU/mRERERETkIVZCFy9xBCVzIiIiIiJSbN27oKH8jwagioiIiIiIPIS0mqWIiIiIiBRbPyxKcEg9jwYFOqSe4kTDLKVIZJ89b2j9LjWrA3BxzgJD4zz2+gguvGl/03RHqToh1PB7gbz7Sf/0H4bH8ej0It/9sXCLERXWExvzVnO9lXLK0DiudfL2lUxu19XQOA12bubHJSsMjQFQKWAAKf0CDI9TJ2kJ5ydNNzxO9Tcmci48ytAYNWZOATD8udVJWgLAtc2fGhqnXNdOXJgxz9AYAFXHj+Hapq2GxynXvTMXZ8caHuexsSFFtmWA0ffz2NgQoIi2wUhYbmgMgEqBA4vs+yb7zFnD47g8XpMUv1cNjVEn8S+G1v+LOWkwoT1K5kREREREpPjSnDm7lOaKiIiIiIg8hNQzJyIiIiIixZZWs7RPyZyIiIiIiBRfmjNnl56MiIiIiIjIQ0g9cyIiIiIiUnxpmKVdSuZERERERKT40jBLu5TMiYiIiIhIsWVyUs+cPUpzf6aEhAS2bNkCQGBgIDdu3GDAgAGW92fNmsXOnTvx88vbKLlevXqsW7cOwFL21VdfERgYyIgRIzCbzRw/fhyAffv2UadOHbKysiz1Dx8+nD/96U98+mne5rGtWrXCbDZjNps5cuQIAG+99ZZVG+5tr7e3NwBZWVnUqFGD7du38+OPPzJ48GBGjBhBQEAA3333HSkpKbRo0YJRo0bx5z//mW3bttmNea+srCyuXbtmdeTfh4iIiIjIwyIuLo66devi7u5OixYt2LVr1wNdl5iYiMlkolevXga3UD1zDuPs7Ey5cuU4d+4cNWrUYPfu3YwbN464uDgAmjdvzrJly/jjH/9ouWb69Ol89NFHuLq6AnD79m0Ali1bRmRkJOvXr8fX1xeAefPmceXKFWbOnEmnTp2oW7cu8fHxVm04fPgwNWvWtLThXvXq1WPfvn2cPHmSdu3aARATE0NYWBheXl6kpaUREhLC1KlTeemll5gxYwa5ubn07NmTjh072ox5r5iYGKZMmWJVFhUVxcShwwrzOEVERERE8vwKc+ZWrlzJ6NGjiYuLo02bNixcuJA//OEPHDt2DE9PT7vXff/994wdO5bnnnuuSNqpnjkHCggIYMmSJezZs4fWrVtbvefs7MzgwYNZtGiRpaxUqVK4urqyd+9eXn31VTZu3MjNmze5cuUKAQEBbNiwwXLu66+/TocOHQgKCgLg5MmTll6yM2fOsGfPHpo3b87AgQNZsmSJzfb179+fpKQkduzYQfv27QE4c+YMXl5eAJQvX57s7Gyra0wmE3Xr1uXSpUsFYtoSERFBWlqa1REREVHIJykiIiIi8v9MTo45CmHu3LkMHjyYIUOG0KhRI+bPn0+tWrV477337F6Tk5PDyy+/zJQpU3jiiSd+6V0/EPXMOdCzzz7LzJkzSUlJISoqqsD73bp1w8/Pj/T0dCCvJ+7WrVv4+PiQmZnJ8ePHWb16NRcuXCAkJIQjR45w6tQpAObMmcPx48dZtWoV0dHRBXrJpk6dSkZGBt988w2HDh2ymUCVKVOG7OxsatWqZdl8sWbNmpw4cYKGDRty7do1XFxcClyXkpJClSpVHqhnzs3NDTc3twLl2TbOFREREREpKllZWQWm/9j6b9dbt25x8OBBxo8fb1XeqVMn9uzZY7f+qVOnUqVKFQYPHvzAQzJ/KSVzv0BsbCzr169n7dq1liSnbdu27N692+YwR4CwsDBatWoFwMSJEwkKCqJcuXLcvHmToUOHMmPGDDZu3Ii7uzv79u0jISHBcu3TTz/N22+/zcWLFy29ZAADBgzgypUrJCUlAf+br5c/lPJuc+bMwWQyWXoIx48fT3h4OB4eHly9epXJkycDsG3bNm7evEl6ejojRozAZDJZxXz11Vdp2bKlA56iiIiIiIh9jloAxd50oOjoaKuyy5cvk5OTQ9WqVa3Kq1atSmpqqs26P//8cz744AO+/PJLh7T1QSmZ+5kCAwMJDAwEsOqtCgsLIywszPJzYmKi1T9btGhhmRvXtGlTq2QNYO3atZbXLVu2LJAwffjhhwD861//sirPHzYJMG7cOJvtvVt+UgbwwQcfFDj/4MGDBcrujSkiIiIiYjgHzZmLiIggNDTUqszWiLL/hbWOm5ubW6AMID09nVdeeYXFixdTuXJlh7T1QSmZK6G2bNnC3r17LT9PmDDBstCKiIiIiMhvjb3pQPeqXLkyzs7OBXrhLl68WKC3DuDbb78lJSXFaqHDO3fuAHlrZPz3v/+lXr16v7D1timZK6G6dOlCly5dfu1miIiIiIj8MoVcvOSXcnV1pUWLFmzbto3evXtbyrdt20bPnj0LnO/l5VVg267IyEjS09N5++23qVWrlmFtVTInIiIiIiLF16+waXhoaCj+/v54e3vTunVrFi1axKlTpyxTlQYNGkTNmjWJiYnB3d2dxo0bW11foUIFgALljqZkTkREREREii1b89SM5uvryw8//MDUqVM5f/48jRs3ZvPmzdSuXRuAU6dO4eT06+/ypmRORERERETkHsHBwQQHB9t8b/v27fe99t5FDo1iys3NzS2SSCIiIiIiIoWUtn6TQ+op36u7Q+opTtQzJyIiIiIixVcxGM5YXCmZkyKRnp5uaP0eHh4AXF259ifO/GUq+P6J9L/vMDQGgEeH9lyOK7j/n6NVDh7Mjd17f/rEX6hMWx/OR0z56RN/geoxUQBkp14wNI5Ltbwlic+FRxkap8bMKdw89JWhMQAead6UC2/ONTxO1QmhnJ803fA41d+YSPJzfzA0RoNdfwPg0tvxP3HmL1NlVN4k+4x9Bff9dKTSLVtw/R87DY0BUPbFdtzYe8DwOGV8vLmyYo3hcSoO6MOPGZmGxqhU2h2AS7ELDY1TJWQYUDR/qzMOHDY0BkBp72ZF9n2Tfeas4XFcHq9ZZJ8BefgomRMRERERkeKriLcmeJgomRMRERERkWLr11jN8mGhZE5EREREkPY0CgAAIABJREFURIqvX2GfuYeF+ixFREREREQeQuqZExERERGR4kvDLO1SMiciIiIiIsWXFkCxS09GRERERETkIaSeORERERERKbZMWgDFrt98MpeQkMDatWt5/PHHcXJyYsGCBYwZMwaTycTcuXPJzc1l+PDh5OTkABATE8PYsWNxcXHB2dmZl156ifT0dBYsWMCBAwfIysqibt26LF++nO3bt3P8+HEqVKhA8+bNadiwIf7+/hw/fhxnZ2fMZjNPPvkk33//PZ999hkdO3akb9++dOzYER8fH8aOHUvfvn3Zvn07U6ZM4YknnqB8+fLMnTuX6Ohoq7qDgoI4f/48v//97/n3v/9N9erVC9yru7s7u3fvxtvbm9dff50ffviBhIQE5syZw4kTJ8jJyeG5554jICCAbt268cQTT5CTk0OlSpWYNm2azZj3ysrKIisry6rMzc3NmF+eiIiIiJR8mjNn128+mQMIDg6mS5cu9OrVi8zMTNLS0gDIzMwkIyOD9PR0Pv74Y6trYmNjcXd3B/ISwnr16rFv3z5OnjxJu3btLOdFR0fj5eUFwPbt2+nQoQNz584lLCwMgIiICAD8/PyIj48HYO/evfTt25eNGzfSt29fAHx9fTGbzfj6+tqsG2DJkiXMmTOHpUuXEh4eXuA+O3bsyKpVq2jcuDE3b94E4MiRI/zwww8sXLgQgGHDhtG5c2c8PDyIjY0FYNGiRWzYsMFmzHvFxMQwZcoUq7KoqChef/11u9eIiIiIiEjhKZkDFi5cyKpVq6hTpw5r166la9euODk5sW7dOgYMGEC3bt0ICgrC3d2d6dOnAxASEoKzszP9+/cHoH///iQlJXHjxg3at29vqTs6OpoKFSrQoUMHqlSpgo+PD/v27ePChQt227N06VKioqKYMWMGZ86cASApKYl169bRpk0bm3X369eP/fv3s2bNGkub7lW2bFlu377NihUr6NWrF8uXL+c///kPLVu2tJzz9NNPk5ycbHXdM888w7Zt22zGvFdERAShoaFWZW5ubty6dcvu/YqIiIiI2OWkZT7sUTJHXm9Uly5dGDVqFB9++CGenp4AXLp0iQEDBjBw4EAGDhxIYmKipYfq3p65MmXKkJ2dTa1atax2qb+3Zw5gwoQJvPnmmzbbkpGRwc6dO4mKiuLSpUskJCTQtm1b+vXrx9ChQ/H397ckRnfXvWvXLs6ePYvZbOb06dPs3r2btm3bFqi/d+/ejBkzht27d7N8+XIaNWrEypUr6dWrFwBfffWV5XW+AwcO4OXlxaFDh36yZ87Nzc3msEolcyIiIiLyc5g0zNKuQiVzOTk5LFiwgOTkZBYsWMC3337L999/z4svvmhU+4pEXFwcmzZtIi0tjQYNGhAXFwfAiBEjOHToEO+++y5ly5bl/PnzzJkzh23btll65u5OmObMmYPJZGLRokWWsvyerEaNGtG0aVMA6tevT+nSpQvMLYO8HrjIyEj8/PwA6NGjh6U3ztnZmZdffpnFixcXqPvIkSOsWbOGmjVrcu7cOaKjo20mc8899xxffPGFZQ5gkyZN2LJlC8HBweTk5ODj40P16tVJT08nJCSEO3fuUKlSJYYOHWpJ5vJjjho1yhGPX0RERETEPvXM2VWoZC4kJITs7Gx2794NwKOPPoqfnx/79+83pHFFITAwkMDAQJvvLViwAIAPPvjAqjwhIeG+dZrNZgCef/75Au/ll82bN8+qPDExEYCAgACr8vyewBdeeAGAbt263Tc2QI0aNawSyntjuLi44OLiYrmP/Pl7d/vkk08KlEVHR/9kbBERERERKRqFSub27NnDl19+SbNmzQCoUKGChs8VU6mpqZYFVQC6du1qNTdOREREROShoGGWdhUqmcufI5YvJyeHO3fuOLRB4hjVqlVTT5qIiIiIPPy0z5xdhRqA+vvf/56PP/6Y3NxcUlJSCA4OtlqGX0RERERERIpGoZK5uXPnsnPnTs6fP0+rVq24c+cOs2bNMqptIiIiIiLyG2cyOTnkKIkeeJhlTk4OW7duZeHChZYNpkVERERERAylOXN2mXJzc3Mf9OQ2bdrw+eefG9keERERERERixtf7HNIPWVal7zFAAu1AIq3tzdffPEFrVu3Nqo9UkJlnz1vaP0uNasDcGn+e4bGqTJ6OOcnvmFoDIDq0ydx5eNVhsep+HJ/ru8w/n/QlG3fhu8HDjE0Ru3l7wOQfeasoXFcHq8JQEr/Pxsap86qD/lh4YeGxgB4dNifOfXqCMPjeP5lAVdWrDE8TsUBfYrkewAguW1nQ+M02L0VgGubPzU0TrmunYrse83oe4G8+7n0dvxPn/gLVRllJj093dAYHh4eAIb/Paj4cn+AIrmfovpeu7I8yfA4FQf2M/y/byDvv3GS23U1NEaDnZsNrV+MU6hkbufOnbz77rs0bNiQsmXLWsr37XNMtiwiIiIiImKlhM53c4RCJXPz5883qh0iIiIiIiIFmLQ1gV2FSubat29vVDtERERERESkEAqVzL3wwguYbKwm849//MNhDRIREREREbHQapZ2FSqZGzt2rOV1ZmYmy5cvp379+g5vlIiIiIiICKBk7j4Klcx169bN6ueePXvStauxq+uIiIiIiIhIQYVK5u51584dTp486ai2iIiIiIiIWDE5aTVLewqVzPXr188yZy4nJ4evvvqKTp06GdIwERERERERlMzZVahkrnv37v+7sFQpwsLC8PHxcXijHhYJCQkkJSVRq1YtateuTVpaGjNmzLD53q1bt+jWrRve3t5kZWXxyiuvkJSUxL59++jfvz///e9/OXHiBO+++y4HDhygfv36VKtWjcmTJxMWFsYjjzxCeno6UVFRODk50adPH1q0aEFmZiZLlizh2LFjREdHU6VKFWrXrk14eLhVW6Ojo/n6669ZvXo1qamp1K9fn6+//prc3Fyio6Px8PDg5s2bzJw5k6+//popU6bw+9//nrS0NEaOHEnz5s2pX78+HTt2BGDy5MnUqFGjwDPJysoiKyvLqszNzQ39KygiIiIiP4vmzNlVqGSuatWqdOnSxapsy5YtBcp+S0JCQujSpQspKSnEx8fbfA/g1KlTzJo1C29vb9atW0fPnj0BWLZsGZGRkaxfvx5fX1/i4+OJjo7Gz88PLy8vwsLCCAsLw8vLi7S0NEJCQpg6dSovvfQSM2bMwGw2c/XqVT799FOGDh16355SNzc3zp8/z0cffWSZ/zhlyhTmzp3Lo48+ynfffcf06dPp2bMnvr6+mM1msrKyGDBgAGvXrsXb27vAPd4rJiaGKVOmWJVFRUUxceiwQj9bERERERGxr1AdJhMmTHigst+S2NhYzGYzW7ZssftefHw8np6eXLx4kczMTNauXUufPn24efMmV65cISAggA0bNtis/8yZM3h5eQFQvnx5srOzAfj73/9Onz59yM7OpmLFigwePJgdO3YwdOhQ3n//fZt1+fv7s2zZMk6ePEndunWBvJ60Rx99FIAnnniC1NRUq2vc3Nxwc3MD4MCBA5jNZsxmMzdv3rQZIyIigrS0NKsjIiLipx6jiIiIiIhtTibHHCXQA/XMffPNN5w4cYJr166xefNmS3laWhoZGRmGNe5h8KA9cwB/+tOfmD9/Po899hiPPPIIy5Yt48KFC4SEhHDkyBFOnTqFp6enVR01a9bkxIkTNGzYkGvXruHi4gJAhw4dmDFjBkOHDuXy5ctUrlyZ6dOnA/DHP/6RwYMHF9gTsE6dOixevJjevXvz9ddfA3nJ2o8//kilSpVISUmhatWqVtfcunWLW7duATxQz9zdyd/dsu97lYiIiIiIbSaTJuzY80DJ3Oeff05CQgIXLlzgrbfespSXK1eOOXPmGNa4h0FsbCzr168nIyODI0eOcPXqVQB8fHws7z322GNMnTqV3r174+npyaZNmwBYt24dGzduxN3dnX379pGQkMDkyZOt6h8/fjzh4eF4eHhw9erVAu+PGjWKt956i9atW7N161acnZ1p2LChzc3dAVauXImzs7Olt2zy5MmEhoZSoUIF0tPTmTFjBkePHmXlypX85z//4dq1a0ycOBH4X88cQFhYGPXq1XPcgxQRERERkUJ5oGQuICCAgIAAPvjgAwYPHmx0mx4agYGBBAYG3vf9u7m5uXHhwgXLz2vXrrW8btmyJS1btgTyFivJV7lyZT744IMCdecvtNK4cWNmzpwJQK9evey25e46774e8hZrudvzzz/P888/X6COb775xm79IiIiIiKG0AIodhVqAZTBgwdz4cIFjh49SmZmpqVcG4cXP4mJiRw/fhyA0qVLM27cuF+5RSIiIiIiP0MJne/mCIVK5pYsWUJ0dDQ//PADDRo04KuvvsLHx0fJXDHk5+f3azdBREREROSXU8+cXYWaTThnzhwOHTpEvXr1OHjwIP/4xz8sKy2KiIiIiIhI0SlUMufi4kLFihW5ffs2AO3atePYsWOGNExERERERMRkcnLIURIVapilm5sbubm5NGzYkNjYWGrXrs3ly5eNapuIiIiIiPzWac6cXYVK5qZNm8a1a9eYNWsWZrOZq1evEhcXZ1TbRERERERExA5Tbm5u7q/dCBEREREREVuyvj3pkHrc6tV1SD3FSaF65s6ePctrr73G6dOnOXjwIF9++SXbt29n9OjRRrVPSojsM2cNrd/l8ZoAXFmeZGicigP7kfzcHwyNAdBg19+48cU+w+OUad2SG5//y/g4bVpx4c25hsaoOiEUKLrPWurUmYbGqTY5nDMjwgyNAfD4gre4OOsdw+M8Nm6k4b8byPv9pH/6D0NjeHR6EYDzE98wNE716ZMAuPR2vKFxqowyk9y2s6ExABrs3sql2IWGx6kSMoyrq9YZHqdC/96kp6cbGsPDwwOAjH0HDY1TumULAH7MyPyJM3+ZSqXdOTMy3NAYAI+/M5Psc6mGx3GpUY2s5G8Nj+PWoB7nxkcbGqPGDGPr/6VMWs3SrkLNBBw2bBh9+/a1LIDSuHFjmxtai4iIiIiIiLEKlcylpqbyyiuv4OSUd1mpUqUoVapQnXsiIiIiIiIPzsnJMUcJVKhMrFSpUtw9xe7KlSvcuXPH4Y0SEREREREBtGn4fRQqRe3Xrx9ms5n09HQSEhLo3LkzgwcPNqptIiIiIiLyW2cyOeYogQrVM/f666+zYsUKrl69yubNmxk5ciSvvPKKUW0TEREREREROwo94W3AgAEMGDDAiLaIiIiIiIhYMWnTcLseaJhly5YtLa8nTZpkWGNERERERESsmJwcc5RAD3RX2dnZlteffPKJYY0RERERERGRB/NAyZw26nswCQkJdOvWDbPZTPPmzTl+/LjlvcOHD+Pv709ISAjBwcFkZmaSlZVFcHAwI0aMYNCgQRw4cACAhg0bMnz4cPr06cONGzdITU2lf//+DB8+nHHjxgHg5eXF6tWrAZg9ezadO/9vw1cfHx/Le9988w3Dhw8HICoqis8//9xm2+++ZujQoaSm5m22ef78eYKCgrhy5QrDhg0jJCSE4cOH89e//tVmPVlZWVy7ds3qyMrK+tnPVERERER+47QAil0PNGfu0qVLxMXFFXidLzg42PEte0iFhITQpUsXoqOjrcqnTZvGihUrcHV1ZceOHcTFxeHq6krPnj3p3Lkzt2/fxtfXlzVr1tC8eXPee+89YmJiSE5O5uTJk3Ts2JGgoCBLfU2bNuWf//wnffv25bvvvqNixYoA7N27l759+7Jx40b69u1L/fr1ady4MZMmTSI7O5s2bdoUaPO91wwaNIglS5YQHh7OkiVLGDRoEDExMYwdO5YGDRoAWDaOv1dMTAxTpkyxKouKimLikKG/5LGKiIiIyG+V5szZ9UA9cx07dmT//v3s37/f6vX+/fstvUmSJzY2FrPZzJEjR6zKXV1dcXV1BfLmIB49epRjx47RqlUrIG8PP3d3d3Jzczl8+DCDBg1i165dNGnShG7dunH58mWCgoKYMWMGkNdbWqNGDVavXo2Pj48lztKlS/H396dSpUqcOXMGAD8/P+bNm8drr71ms833XvPcc8/x+eefk5uby549e2jbti1nzpyhQYMGXL58mdGjRxdI2PJFRESQlpZmdURERPyyhyoiIiIiv1kmk5NDjpLogXrmPvzwQ6PbUWLY65nLzs4mOzsbFxcXDhw4wFNPPYWrqyv79+/npZde4vbt22RmZmIymWjWrBlLly4lIiKC5ORkvLy8mDBhAgBms5mzZ88C4O/vT7t27Th27BibN28mIyODnTt3EhUVxaVLl0hISCAyMpLx48ezYcMGoqKi+Mtf/mLVLnvXPPvss8yaNcvSk/f444+TnJxMgwYNmD9/Pn5+fjbv383NDTc3twLl2TbOFRERERGRn6/QWxPIg4uOjqZChQq0bt2aiIgIhgwZQsWKFcnKymLu3LmYTCZCQ0PZtGkTaWlphIeHW10fGhpKWFgYQ4YMYdmyZbi7u3Pr1i2qVasGgKenJ8nJybi4uACQlJREZGSkJdHq0aMHSUlJNG/enBdffJHk5GRWrVpF//79LTFsXZObm0tAQAANGjQgOTkZyOtxCw8Px9XVFScnJzp16mT48xMRERER0TBL+5TMOVBgYKDl9b09cwBLliwpUHbv/EOAxMREAKpUqUJCQgIAbdu2tXlOfiKX//PdNmzYYPXzsGHDCpwTEBBg85rq1atz/fp1S3nFihVZtGhRgetFRERERIx0073gqK+fw8MhtRQvSuZ+Y1JTU4mPj7f83LVrV6t9BEVERERE5OGgZO43plq1ajZ7DUVERERE5OHyQMu6PPPMM7Rs2dLuISIiIiIiUpLExcVRt25d3N3dadGiBbt27brv+WvWrOGpp57Czc2Np556inXr1hnexgfqmZs9ezYAmzZt4sSJE7z66qtA3ibZTZs2Na51IiIiIiIiRWzlypWMHj2auLg42rRpw8KFC/nDH/7AsWPH8PT0LHD+F198ga+vL2+88Qa9e/dm3bp19O/fn927d1u2IjPCA/XMtW/fnvbt23PgwAHWr19Pz5496dmzJ6tXr2bHjh2GNU5ERERERKSozZ07l8GDBzNkyBAaNWrE/PnzqVWrFu+9957N8+fPn89LL71EREQEXl5eRERE0KFDB+bPn29oO025ubm5D3pygwYN+Pe//80jjzwC5O1R1rRpU8vy9SIiIiIiIo6Unp7ukHpcXV3JysqyKrO1R/KtW7coXbo0SUlJ9O7d21I+atQovvzyS5udWZ6enowZM4YxY8ZYyubNm8f8+fP5/vvvHdJ+Wwq1Fbqvry+tW7cmJiaGmJgY2rRpY3fzaBERERERkeIiJiaG8uXLWx0xMTEFzrt8+TI5OTlUrVrVqrxq1aqkpqbarDs1NbVQ5ztKoVaznDZtGq1atWL79u3k5uYybdo0unXrZlTbpATJPmfsB9mlRt5G6hdnvWNonMfGjeTCm3MNjQFQdUIoF+csMDzOY6+PIP3Tfxgex6PTiyQ/9wdDYzTY9TcAbn55xNA4jzzdBIDvuvU3NM4Tn6zix2UrDY0BUMnfl287/8nwOPW2riU1quAfTEerNiWCc+OjDY1RY0Ze/d+/PNTQOLU/XgzAtU1bDY1TrntnLkyfbWgMgKoTxxp+L5B3Pxdnxxoe57GxIQ7rLbDHwyNvV6xL820P63KUKqOHA47r/bDHw8OjyL7XUt+YZXicapPGcSvllOFxXOt48v3AIYbGqL38fUPrLy4iIiIIDQ21Kru3V+5uJpP1ZuW5ubkFyn7J+Y5Q6K0JunbtSpMmTahTp44BzREREREREXE8W0MqbalcuTLOzs4FetUuXrxYoPctX7Vq1Qp1vqMUapjlrl27qF27Nu3atQNg//79+Pv7G9IwERERERGRoubq6kqLFi3Ytm2bVfm2bdt49tlnbV7TunXrAud/+umnds93lEL1zI0bN44dO3bQt29fIG//uUOHDhnSMBERERERkV9DaGgo/v7+eHt707p1axYtWsSpU6cwm80ADBo0iJo1a1rm3I0aNYp27doxc+ZMevbsyV//+lc+++wzdu/ebWg7C5XM3b59m3r16lmVubq6OrRBIiIiIiIivyZfX19++OEHpk6dyvnz52ncuDGbN2+mdu3aAJw6dQonp/8Ncnz22WdJTEwkMjKSSZMmUa9ePVauXGnoHnNQyGTO3d2d69evWybyHT16FHd3d0MaJiIiIiIi8msJDg4mODjY5nvbt28vUNa3b1/LCMaiUqhkbtKkSXTu3Jlz584RGBjIli1b+Oijj4xqm4iIiIiI/MZlO7v82k0otgqVzHXq1IkGDRqwZcsWcnNziYyMpH79+ka1TUREREREROwo1GqW06ZNo27dugwfPpzg4GDq16/PtGnTjGrbQ2Hfvn3UqVOHrKwsAgMDGT58OH/6058sq9msWrWK1q1bW87v1q0bI0aMoGfPnnz11VcsWrQIs9lM/fr1MZvNrFy5Ei8vL1avXg3A7Nmz6dy5M4DlHLPZzLlz5+jQoQPz5s0DYPz48Rw5cgSz2Yy3tzd+fn6MHj2a7du3079/f4KDg3n/fdt7iLz11lsMGDAAgB07djB79v/2G/Lz8+PGjRvExsYSFBTEiBEjCA8Pd/yDFBERERGxITfXMUdJVKieubVr1xIZGfmTZb8ly5YtIzIykvXr1wMwb948fvzxR2JiYnjppZfYuHEjffr04V//+hetWrXCw8ODBQsW8MUXX/DPf/6T0aNHA3lJU3x8PJD3TP/5z3/St29fvvvuOypWrAiAt7e35RyAKlWqcODAAa5evQrkbcYZHx9PdHQ0fn5+eHl5MWrUKKZNm0bDhg3t3sPhw4epWbMm586do3379syZM4exY8dy7tw5KlSowMmTJ7lw4QKLFi0C8hbCsScrK4usrCyrMjc3t8L9XwMRERERkf93p6RmYg7wQMnctm3b+PTTTzl37hzjxo2zlKelpRnWsIfBzZs3uXLlCgEBAQQGBuLi4kJYWBgZGRmMHTuWM2fOUKlSJQYNGsSUKVNo1aoV6enpvPbaa+zatYtPP/3UZr0mk4kaNWqwevVqfHx82Lx5MwAHDhywLIea3yMXHh7OzJkz7bYxPDycOXPmcPXqVXr06EHPnj2t3t+zZw/NmzfnxRdfZMmSJURERODj42NJNgMCAjh27Bje3t4AvPPOO2zbto01a9bYXMk0JiaGKVOmWJVFRUUxMcj8gE9VREREREQexAMlc66urpQtWxaTyUSZMmUs5dWrVyciIsKwxhV3q1ev5sKFC4SEhHDkyBEqV67M5s2bLSt8vvHGG5w+fZrJkyfz+eefk5GRgYeHB++++y4bNmxg8+bNvPrqqzbr9vf3p127dhw7dsySzN3bMwfw+9//nvfee89uYl2jRg3mzJkDQPfu3QskcwkJCWRkZPDNN99w6NAhIiIiCAgI4I033uD8+fNMmDABDw8PEhMT6dWrFyNHjuTQoUPcuXPHZryIiAhCQ0Otytzc3OCHKz/xNEVERERECspVz5xdD5TMtW/fnvbt29OrVy+aNm1qdJseGuvWrWPjxo24u7uzb98+fHx8LO/l5uayd+9ePvnkEyBv7lz+PDiAHj160KdPH15++eW8ZOcenp6eJCcn4+Lyv9V77u6ZCwsLs5RPnDiRRo0a8eabbxao5/333+fw4cPcuXOH5557zuq9GzducOXKFZKSkgCYNWsWO3fupF27dpw/f562bdsC0LhxY6pWrUpQUBClS5fGzc3Nql13c3Nzs3k/2TbPFhERERG5PyVz9hVqzlzTpk1Zu3YtX375JZmZmZbyWbNmObxhD4O1a9daXrds2dKqt8pkMlkSOYD+/fsDebvF51uzZo3ldWJiYoHX+QlT/s/ffPONVfz88scff5z09HRLeXR0tOX1kCFD7La/TJkylkQOsBpC+9e//tXq3JCQELv1iIiIiIhI0StUMjd69Gi+/fZbDh48yIABA0hKSuKll14yqm1igPj4eFJTU4G8IZhBQUG/cotEREREROzTAij2FSqZ+/vf/85XX31Fs2bNmDNnDuPGjbtvz48UP/nDNEVEREREHgbK5ewrVDLn7u6Ok5MTJpOJ7OxsqlatytmzZ41qm4iIiIiI/MZpzpx9hUrmPDw8yMjIoG3btgQEBFCtWjW7C2GIiIiIiIiIcQq1l/OKFStwdnbmrbfe4ne/+x1OTk5WC2iIiIiIiIg40h1yHXKURIXqmatatarl9cSJEx3eGBERERERkbtpmKV9ptwHeDr9+vXDZDLZfX/VqlUObZSIiIiIiAjAqR/THFKPZ6XyDqmnOHmgnrnu3bsb3Q4p4W5fumxo/aWqVAbg+vbdhsYp+3xbbnyxz9AYAGVatyTz6HHD47j/zovkdl0Nj9Ng52bS/77D0BgeHdoD8H/s3XtYVWX6//E3EkIqonhIMUslDStrUkM0VCxNRhzNRCCNgzHqViEnlZDGEeybg9pYTR6GtGZAG7XwUJqOjU0hWhJqWpShTGKGiaOpgCIbkP37g5/7agdbJVkI9nld17pkP2ut537WghbdPIdVvHe/oXGa9H4QgKLtHxsax3XIIEqyDxsaA8DFq5vh1wL//3rq6Gf6/M5PDY3RrH8/AM7v+MTYOAMfBqD0yFFD4zTu0okLGXsNjQHQ1Kc35m9zDY/j7NmZ8x+lGx6n2SMDbN7xagRXV1cASr76xtA4Lvd1B6iT6yn55pChMQBcut9t+D2Dyvtm9D2Dyvt2YVeGoTGa+voYWv/10qsJ7LumZC48PNzmc3l5ObfcUqMRmiIiIiIiIjVWUaFkzp4aLYBy8OBBfvOb39C5c2cA9u3bR2xsrCENExERERERsVhqZ7sZ1SiZmzp1KkuWLKF168ohbT179mTLli2GNExERERERETsq9FYyaKiInx9fa2fHRwc9J45ERERERExjFZgx0y7AAAgAElEQVSztK9Gydwtt9xCWVmZdWXLvLw8GjWqUeeeiIiIiIjINbtZ3xFXG2qUiUVFRTFq1ChOnz5NQkICAwYMICYmxqi2iYiIiIiIiB016pl76qmn6NKlC++99x7FxcWkpKTQv39/o9omIiIiIiK/chpmad81J3OXLl0iIiKCVatW0a9fPyPbJCIiIiIiAiiZu5JrHmbp6OjI8ePHjWxLnUtOTqZXr15UVFSQnZ1NQkICs2bNYurUqYSHh7Nt2zYAPD092bhxIwAhISE2/14WEhJCSkoK69ats5aNGDGCjz/+mKSkJNLS0ujYsSMXLlygpKSEiIgIABYvXszEiROJioqyec3Ds88+y/Tp062fu3XrxuTJkxk9ejQXLlwgLS2NAQMGYDKZmDlzpvU4Hx8fmzb81NChQ3n55ZcBSE1NxcvLC4Dt27cTFhbG1KlTiYmJwWKxkJCQwFNPPcW0adOYMGECP/74o92YP2U2myksLLTZzGbz1b8ZIiIiIiLVqLDUznYzqtGcucGDBzN58mQyMzM5ePCgdWvIevTowVtvvQXA+++/T+fOnVm6dCkpKSm88cYblJeX07NnT1atWkV5efkV6xozZgypqakAZGRk4O3tbV0sBuDRRx+1JlMAX331FSdPnmT58uUsWbKEefPmAVBSUkJBQQHnzp2jpKQEqHwNxN/+9jd69+5NTk4OAGPHjiUpKYm//OUv1piBgYFs3ry52va1bNnSem5aWhoPPPAAFRUV1utdunQp9913nzVxnT17Nn/961+ZOXOmtW0/j/lziYmJuLm52WyJiYlXvG8iIiIiIlJzNUrmVqxYwbZt2wgODiYgIICAgACGDx9uVNvqRHBwMJs2baKkpISDBw/Sp08f6z5PT09OnDiBo6MjkZGRLF++/Ip1NWnSBHd3d44fP05KSoq19+0yHx8fcnNzOXnyJFD5EvbevXsD8NprrzFq1ChKS0vZsGEDw4YNY/jw4dbEav/+/YSFhbFz50569OgBwOrVqzGZTNZEa+XKlYSGhuLu7k5eXl61bfTx8WH9+vV4eHjg4ODA6dOn6dSpkzXp9Pb25uuvv7Y55+677+bEiRPVxvy5uLg4CgoKbLa4uLgr3jcREREREXssFkutbDeja54zZ7FY+OSTT/Dw8DCyPXXOwcGB6OhoXnvtNbp3786ePXv4zW9+A8CRI0do164dAAEBAYSEhFBUVHTF+sLDw1m2bBlnz57l9ttv57///a/N/ueff54///nPANxzzz2sXbuWxx9/nGeeeYbPP/+ciooK1qxZQ9u2bQE4deoUTz75JA8++CArV64kLi7OpmfOZDIBUFxcTHp6OvHx8Zw6dYrk5GRmz55dpX1jxozhnnvuIT09neeee47WrVvz3XffWffv3buXe+65h6ysLGvZ4cOHad++fZWY1XF2dsbZ2blKeTlXvm8iIiIiItW5WROx2lCj1SyHDRvGgQMHjGrLDTNw4EBeffVVhg8fTk5ODs888wznz59n/PjxNi9Fj4mJsfbcFRYWWpOa8PBw6zE+Pj5MmjSp2kQK4K677qJJkyaYzWbuu+8+brvtNiZOnEiTJk1wdnbm+PHjdOzYkWXLlgGVr4M4evSo9fzp06cTExNDREQEq1ev5sCBAzg6OuLt7c3s2bOtc/lGjBiBxWKxGeYJlb2HOTk51utq1KgRJpOJ8ePH07x5c5ycnHjppZfIysrixRdfpFWrVpSUlPDnP/+ZrKwsm5hLly69zjsvIiIiIiK/lIOlBqnu6NGjWb58Oa1atTKyTXITKj912tD6b2nTGoDzabsMjdPMz5cLuzMNjQHQtK83JV9nGx7H5V4vcgYMMzxO1/StFP1nh6ExXB8dCEDx3v2GxmnS+0EAirZ/bGgc1yGDKMk+bGgMABevboZfC/z/66mjn+nzOz81NEaz/pUrOp/f8YmxcQY+DEDpkaOGxmncpRMXMvYaGgOgqU9vzN/mGh7H2bMz5z9KNzxOs0cGXHW0zvVydXUFoOSrbwyN43Jfd4A6uZ6Sbw4ZGgPApfvdht8zqLxvRt8zqLxvF3ZlGBqjqa+PofVfry+/z6+Veu7v2K5W6qlPatQz17RpUx588EGGDx9Os2bNrOULFy6s9YbJ9Zs/f751AZV77rmHoKCgG9wiEREREZGaqdAwS7tqlMx5enri6elpVFukls2aNetGN0FERERERAxSo2QuPj7eqHaIiIiIiIhUoQVQ7KvRqwmKioqIjo6me/fu3HPPPUybNq1OxgqLiIiIiMivU4XFUivbzahGydyUKVMoLS1lzZo1rF69mrKyMqZMmWJU20RERERE5FfOYqmd7WZUo9UsH3jgAb744ourlomIiIiIiNSGvbnHa6We3p071Eo99UmN5sxdunSJoqIi61K5Fy5coKKiwpCGiYiIiIiIaM6cfTVK5sLCwvDx8WHcuHE4ODiwdu1amxdmi9hTllc7f1Gxx+n2yr+05PgONTRO110fcHZ1qqExAFqOHWP4e9mg8t1sRr+bDyrfz/e/Ba8aGqNt7B8ADH83m4tXNwD+t/A1Q+O0fe6ZOvsZMPpaoPJ6Ct//wPA4zYcP5eya9YbGaPnkaABOvfo3Q+O0+cNkAMPfAdfUp3edPdfq6j2dOf1/a3icrjv/VWfvmauLd6gCnCkuMTSOexOXOnuvZeG2/xgep7n/o5R+973hcRrf2bHOnjf11c0636021CiZe+6557j//vv58MMPsVgsLFiwAH9/f6PaJiIiIiIiInZcUzJXWFjImTNn6NSpE/7+/tYELjc3l8LCQpo3b25oI0VERERE5NdJwyztu6bVLJ977jn27dtXpXzXrl16MbWIiIiIiBhGq1nad03JXHp6OqNHj65SHhoaSnp6eq03SkREREREBPSeuSu5pmTO0dHR7j4HB4daa4yIiIiIiIhcm2uaM1deXl7t3LiCggLKysoMaZiIiIiIiIjmzNl3TT1zTz75JKGhoZw9e9ZadvbsWcaPH09ISIhhjRMRERERkV83DbO075qSuT/+8Y+0aNGCjh078uCDD/Lggw/SsWNHXF1d+dOf/mR0G0VERERERORnrnnOXEpKCl988QXPP/88zz//PF988QUpKSlXnE93MzGbzURFRREVFcXEiRP55JNPCA8PJyoqisjISE6fPk1aWhodOnTAbDYD0KtXL5KTk0lOTqZ3797Wejw8PEhLS+PMmTNERkYSFRVFeHg4R44c4ejRo7i6unL8eOVLtn/a8+nj48O6desASEtLY9CgQURGRjJ9+nQAEhISCAkJwWQysXz5cgBOnDhBmzZtOHHihN1r+2m9EyZMID8/33ruxIkTOXv2LJMmTSI6OprJkyfz3nvvXfE+FRYW2myX74eIiIiISE2pZ86+Gr003NPTE09PT6PaUq+tWLGCkSNHMmTIEADGjh3L4sWLadWqFUeOHGHevHmMHDkSX19f3nvvPe688066du1qPd/T05PMzExyc3MZMGAAAImJicTExODl5UVBQQHR0dG88MILDB8+nHnz5rFs2TLr+RkZGQQGBrJ582YCAwMBCA4OxmQyERwcbD0uISEBLy8v6+eUlBQWLVrEypUriY2NrXJdP683LCyMlJQUYmNjSUlJISwsjMTERGbOnGm9nvLycrv3KTExkblz59qUxcfH88ffT7jmey0iIiIicpnmzNl3TT1zAgcPHuShhx6yfrZYLLRq1QqALl26WHuz/Pz82LFjB+vWrWPMmDHW44OCgkhNTWXHjh0MHDgQgLy8PGvi5ebmZl1M5s4776R58+ZkZWVZz1+5ciWhoaG4u7uTl5cHQGpqKkOHDuXee++1HpeQkIDJZCI1NRWAPXv2EBYWVu17Aqurt3///nzyySdYLBY+/fRTfH19ycvLo2vXrpw+fZo//OEPVZK1n4qLi6OgoMBmi4uLu8a7LCIiIiIi10rJ3DXq3r27TULk4ODAmTNnADh69Ci33XabtbxDhw6Ul5fTtGlT6/FNmzalrKyMjh07Wl/n0KFDBw4fPgxAYWEhTk5O1uNnzZrFggULACguLiY9PZ34+HiOHTtGcnIyAGPGjGHr1q1kZ2dTWloKVCZzSUlJjBkzhp07d3L8+HFMJhPff/89u3btsrkme/X269ePhQsX8vDDDwNw++23k5OTQ+vWrXn11VfJycmxe5+cnZ1p3ry5zebs7Fyzmy0iIiIi8v9ZLJZa2W5GNRpm+Ws2YcIEZsyYwcaNG7l06RJRUVFMnz6dFi1aUFRUxPz58/n666+BykTMYrGwfft2mzoWLVqEg4ODdT7brFmziI2NxdXVlXPnzjFnzhzrsS1atKBnz55s2bKF1NRUZs+ebZ0/N2LECGui5ejoyLhx41ixYgVQmcy1aNGC7t27k5WVxfr16+nQoQM//PADCQkJ+Pr6WmNUV6/FYiE8PJyuXbtak7a4uDhiY2Np3LgxjRo14rHHHjPiFouIiIiIVFFxc+ZhtULJ3DVycXFh6dKlNmX9+vWz+ezn54efn5/1s7+/f7V1mUwm69dvvvlmlf3z588HYPr06dbFTX5q06ZNAAwaNAiAgICAq7bfw8PDmkReFh4eXm297du35/z589byli1bVjlXRERERKQu3Ky9arVBydyvSH5+PklJSdbPw4YNw9vb+wa2SEREREREfiklc78i7dq1IyEh4UY3Q0RERETkmqlnzj4lcyIiIiIiUm9VoGTOHq1mKSIiIiIi0gCpZ05EREREROotDbO0z8GiuyMiIiIiIvXUlgPZtVJPwG+8aqWe+kQ9c1Inyk+dNrT+W9q0BuDs6lRD47QcO4aif39kaAwA18ce4XTS3w2P09r0NBd2ZRgep6mvDyfi5hoao31iPABlx08YGsepQ3sAfoiNNzSOx4K5FO/53NAYAE0e6kn+/y00PE67Pz1HfsJ84+MkzCJnwDBDY3RN3wrAqb8mXeXI69NmWuVrbC7szjQ0TtO+3nX2XDP6WqDyes69s9HwOC2CRnGmuMTQGO5NXAD48fV/GBqn1aTxAHVyPTfbc60s77jhcZxu71Bnz5v6qkIvmrNLc+ZEREREREQaIPXMiYiIiIhIvaVZYfapZ05EREREROoti8VSK5tRzp49S2hoKG5ubri5uREaGsq5c+eu+dp++9vf4uDgwLvvvlvj2ErmREREREREfqGxY8dy4MABtm3bxrZt2zhw4AChoaHXdO6rr76Kg4PDL46tYZYiIiIiIlJv1eeXhn/zzTds27aNjIwM+vTpA8CKFSvo27cvhw4d4u6777Z77hdffMHLL7/Mnj17aN++/S+Kr2RORERERETqrdoaImk2mzGbzTZlzs7OODs7/+I6d+/ejZubmzWRA/Dx8cHNzY1PP/3UbjJXXFzMk08+yZIlS2jXrt0vjq9hliIiIiIictNLTEy0zmu7vCUmJl5Xnfn5+bRt27ZKedu2bcnPz7d73rPPPku/fv0YOXLkdcVXz5yIiIiIiNRbtbV2SVxcHNOnT7cps9crl5CQwNy5V35H7p49ewCqnfNmsVjszoXbtGkTH330Efv377+WZl+Rkjk7kpOT2bBhA7fffjuNGjViyZIlPPvsszg4OPDyyy9jsViYPHkyly5dAioz/ZkzZ+Lk5ISjoyNDhgyhqKiIJUuWsHfvXsxmM507d2b16tWkpaWRnZ1NixYt6NmzJ926dSM0NJTs7GwcHR0xmUzcfffdfPfdd3z44YcMHjyYwMBABg8ejI+PDzNnziQwMJC0tDTmzp1Lly5dcHNz4+WXXyYhIcGm7okTJ3LixAnuv/9+vvzyS7vjcX9a74QJE/i///s/2rVrx4kTJ4iPj2fBggXMmjWLxo0bU15ejr+/f7V/SbDXfe1Y+98iEREREfkVqKilbK4mQyqjoqIICQm54jGdOnXiyy+/5OTJk1X2nTp1ittuu63a8z766CO+/fZbWrRoYVM+evRo+vfvT1pa2jW1EZTMXdGUKVPw9/fn8ccfp6SkhIKCAgBKSkooLi6mqKiIf/7znzbnLF68GBcXF6AyIfT09CQzM5Pc3FwGDBhgPS4hIQEvLy8A0tLSePTRR3n55ZeJiYkBKv9yABASEkJSUhIAGRkZBAYGsnnzZgIDAwEIDg7GZDIRHBxcbd0AKSkpLFq0iJUrVxIbG1vlOn9eb1hYGCkpKcTGxpKSkkJYWJg1We3atSsA5eXl1d6zxMTEKn/FiI+PZ/bUqCveaxERERGR6tyI98y1bt2a1q1bX/W4vn37UlBQQGZmJt7e3gB89tlnFBQU0K9fv2rPmTVrFr///e9tynr06MErr7zC7373uxq1U3PmruD111/n6aefplOnTmzYsIFhw4YxfPhwNm7ciLu7OwEBAUycOJFnnnmGoqIiAKKjozGZTHz00UcABAUFkZqayo4dOxg4cKC17oSEBEwmE6mpqUBlz1hubm61mf1lK1euJDQ0FHd3d/Ly8gBITU1l6NCh3HvvvXbr3rNnD2FhYezbt++a6u3fvz+ffPIJFouFTz/9FF9fX/Ly8ujatSunT5/mD3/4g91u57i4OAoKCmy2y4mpiIiIiMjNpHv37vj7+zNhwgQyMjLIyMhgwoQJDB8+3Lr4yfHjx/Hy8iIzMxOAdu3acd9999lsAHfccQedO3euUXwlc1cwadIk/v73v2OxWPjHP/7Bv/71L7Zs2cKaNWuAyndKLF++nH79+rFp0yagsmcuKSmJRx55BICmTZtSVlZGx44dbcbNJiQkkJSUxJgxY6xlzz//PH/+85+rbUtxcTHp6enEx8dz7NgxkpOTARgzZgxbt24lOzub0tLSKnXv3LmT48ePYzKZ+P7779m1a9c11duvXz8WLlzIww8/DMDtt99OTk4OrVu35tVXXyUnJ6fadjo7O9O8eXOb7XpWCBIRERGRX7f6/tLwf/7zn/To0YPHHnuMxx57jPvvv59Vq1ZZ95eVlXHo0CGKi4trPbaGWV7BsmXLeP/99ykoKKBr164sW7YMqBxD+/nnn7N06VKaNWvGiRMnWLRoEdu3byc6OhpHR0d8fX2t9SxatAgHBweWL19uLUtISKBFixZ0796dBx54AIC77rqLJk2aVJlzBpU9cLNnz7aO3R0xYoQ10XJ0dGTcuHGsWLGiSt1ZWVmsX7+eDh068MMPP5CQkGDTturqtVgshIeH07VrV2vSFhcXR2xsLI0bN6ZRo0Y89thjtXafRURERETsqa05c0Zxd3fnrbfesru/U6dOV00mf2myqWTOjoiICCIiIqrdt2TJEgDefPNNm/LLvVr2mEwmAPz8/Krsu1z2yiuv2JSvXbsWgPDwcJvyyz2BgwYNAiAgIOCKsQE8PDxsEsor1du+fXvOnz9vLW/ZsmWVc0VERERE5MZRMvcrk5+fb11QBWDYsGHWyZoiIiIiIvVNfe+Zu5GUzP3KtGvXjoSEhBvdDBERERGRa3IjVrNsKJTMiYiIiIhIvVWhXM4urWYpIiIiIiLSAKlnTkRERERE6i0Ns7TPwaK7IyIiIiIi9VTyjj21Uk/EwIdqpZ76RMMsRUREREREGiANs5Q6UXb8hKH1O3VoD8CpV5YaGqfNs1P5YVaCoTEAPOYncGbV24bHcQ8N5nzaLsPjNPPz5WjI04bG6LT27wCUHsszNE7jO24HIDcwzNA4ndet5PSyN69+4HVqPSWS78JMhse5c2USZ9esNzxOyydH18lzACBnwDBD43RN3wpAweZthsZx+50/P8TGGxoDwGPBXArf/8DwOM2HD+XUX5OufuB1ajPNRFFRkaExXF1dATi7OtXQOC3HjgHgTHGJoXHcm7hwOunvhsYAaG16mnOp7xkep8WYkYb//w1U/j9Oju9QQ2N03WX8f5vXQ68msE/JnIiIiIiI1FvK5ezTMEsREREREZEGSD1zIiIiIiJSb2m9RvuUzImIiIiISL2lOXP2KZkTEREREZF6Sz1z9mnOnIiIiIiISAOknjkREREREam3NMzSPiVzIiIiIiJSbymZs0/JnAGSk5NJTU2lY8eO3HnnnRQUFDB//vxq95WWlhIQEEDv3r0xm8089dRTpKamkpmZSVBQEIcOHeLw4cMsXbqUvXv3ctddd9GuXTvmzJlDTEwMt956K0VFRcTHx9OoUSNGjx5Nr169KCkpISUlhYMHD5KQkECbNm248847iY2NrdLed955h1deeYXdu3eTm5vLK6+8wmuvvQZAVFQUM2fOJDMzkw8++IBbb72VS5cusXjxYm65peqPj9lsxmw225Q5OztrPK+IiIiISC1TMmeQ6Oho/P39OXr0KElJSdXuAzh27BgLFy6kd+/ebNy4kZEjRwKwatUqZs+ezbvvvktwcDBJSUkkJCQQEhKCl5cXMTExxMTE4OXlRUFBAdHR0bzwwgsMGTKE+fPnYzKZOHfuHP/+97+ZMGECjz32mN22bt68mdGjR/PZZ5/Rp08fTpw4QUlJCQAnT57E1dWVDz74gDfffBOAS5cu4ejoWG1diYmJzJ0716YsPj6eP06Y9MtupIiIiIj8qmkBFPvUYWKQxYsXYzKZ2LZtm919SUlJ3HHHHfzvf/+jpKSEDRs2MHr0aC5evMjZs2cJDw9n06ZN1dafl5eHl5cXAG5ubpSVlQHwn//8h9GjR1NWVkbLli2JjIxkx44dTJgwgTfeeKPaetzd3QkLC2PlypUAjBo1io0bN7JhwwaeeOIJvv32W3r06AHAW2+9RXBwMN9991217YqLi6OgoMBmi4uLq/kNFBEREREBLJba2W5GSuYMEh0dTVJSkrUHrrp9JpMJgCeeeIJXX32Vtm3bcuutt7Ju3TpOnjxJdHQ0WVlZHDt2rEodHTp04PDhwwAUFhbi5OQEwKOPPsr69etp1KgRp0+fpnnz5sybN48VK1bw3nvvVfnLxj/+8Q++//575syZQ3p6OsXFxTzxxBNs3LiRjRs3MmrUKDw9PcnKygLgqaee4r777uPixYvVXrezszPNmze32ZydnX/5jRQRERGRX7UKi6VWtpuRhlkaZPHixbz77rsUFxeTlZXFuXPnAPDx8bHua9u2LS+88AKjRo3ijjvu4P333wdg48aNbN68GRcXFzIzM0lOTmbOnDk29c+aNYvY2FhcXV05d+5clf3Tpk3jpZdeom/fvnzwwQc4OjrSrVs3HBwcrMdYLBYyMjLYsmULUDl3bt26dYSFhXHbbbcB4OLigouLC4899hiRkZG4urpy/vx52rZta9i9ExERERGRq1MyZ4CIiAgiIiKuuP+nnJ2dOXnypPXzhg0brF97e3vj7e0NQEJCgrW8devW1jlsP3V5oZX77ruPBQsWAPD4449X2w4HBwdrIgcQFBRk/Xrx4sU2xwYHBxMcHGz3mkREREREjKA5c/YpmfuVWbt2LdnZ2QA0adKE55577ga3SERERETEPiVz9imZ+5UJCQm50U0QEREREZFaoGRORERERETqrZt18ZLaoGRORERERETqLaVy9unVBCIiIiIiIg2Qg0UzCkVEREREpJ5auPnjWqnnud8NqpV66hMNs5Q6UVRUZGj9rq6uAFzYnWlonKZ9vTm1+HVDYwC0iZ5EydfZhsdxudcLc863hsdx7upJ4fsfGBqj+fChAJTln7zKkdfHqV3lOxgLNm01NI7biGGGx7gcx+jvDVR+f0q/+97wOI3v7FgnzwGAwq3/NjRO82GPAZDT/7eGxum681919lzL8R1qeJyuuz7gQsZew+M09eldZ7/bSr45ZGgcl+53A3Xzu7qunmulx/IMj9P4jts5k7za8DjuEWPr7HdofaW+J/uUzImIiIiISL1VUaFkzh7NmRMREREREWmA1DMnIiIiIiL1loZZ2qdkTkRERERE6i29Z84+DbMUERERERFpgNQzJyIiIiIi9Zb65exTMiciIiIiIvWW5szZp2RORERERETqLc2Zs09z5q5RWFgYJ06coKSkhDZt2tC3b19MJhMzZ860HuPj48O6desASEtLY9CgQURGRjJ9+nRrWVBQEFOmTOGNN96oEiMiIoLJkycTHh7Om2++CUBISIh1/+Wvf/4vwNGjR+nVqxfTpk1j/PjxbN++vUr9ycnJ9O7dGwCz2YyHhwdpaWmcOXOGyMhIoqKiCA8P58iRI3br69OnDyaTCZPJRFZWVpUYZrOZwsJCm81sNtfgTouIiIiIyLVQz9w1WrhwIXFxcbRv3564uDiaNGmCyWSy7s/IyCAwMJDNmzcTGBgIQHBwMCaTieDgYAA2btzIiy++SLdu3ezGeeWVVwAYP348kZGRNWrjkCFDmD9/PhaLhZEjRzJ48GAcHBxsjvH09CQzM5Pc3FwGDBgAQGJiIjExMXh5eVFQUEB0dDQvvPBCtfV17tyZpKQku21ITExk7ty5NmXx8fHMmDGjRtciIiIiIgIaZnkl6pm7Ru3ataN///5cunSJnj17snr1akwmE/PmzQNg5cqVhIaG4u7uTl5eHgCpqakMHTqUe++9F4DY2Fhef/11IiMjee+996qNExMTQ2BgIKNGjfrFbXVwcKBz586cOnWqyr6goCBSU1PZsWMHAwcOBCAvLw8vLy8A3NzcKCsrs1tfbm6utWfu8nX+VFxcHAUFBTZbXFzcL74WEREREfl1q7BYamW7Galnrga6dOlCaWkpAGPHjrX2zBUXF5Oenk58fDynTp0iOTkZX19fxowZw4QJEwgNDaW0tBQPDw8WLVoEwPDhwxk5cmSVGC+99BIuLi4EBQURFBREeXm5dd/Pk6wrOXr0KG3atKlS3rRpU8rKyujYsaO1165Dhw4cPnyYbt26UVhYiJOTk936rtYz5+zsjLOzc5Xyy/dNRERERERqh5K5X2j16tUcOHAAR0dHvL29mT17tnUO24gRI3j44YcBcHR0ZNy4caxYsQJnZ2f2799PRUUF/fv3r7beZ599loqKCnr06AHAoEGDmKSTekgAACAASURBVDx5MqWlpTz11FM2x17uJQOYPn0627dv5+LFixQVFREVFVVliOVlixYtwsHBgeXLlwMwa9YsYmNjcXV15dy5c8yZMweg2vp+GvPpp5/G29v7em6jiIiIiMgV3aSdarVCyVwN+Pn54efnB0B6errd4zZt2gRUJmIAAQEB11R/cnJylbKpU6dWKVu7di0An332mU35vn37rlh/RESEzeefzvm7vODK1er7eUwRERERESNpzpx9SuZuoKSkJPLz8wHw8PBg4sSJtVr/tm3byMjIsH5+/vnnady4ca3GEBERERGRG0PJ3A30054xI/j7++Pv729oDBERERERI92si5fUBiVzIiIiIiJSbymZs0/JnIiIiIiI1FuaM2ef3jMnIiIiIiLSAKlnTkRERERE6i31zNnnYNHdERERERGRemrGqk21Us+i0BG1Uk99op45qRNlx08YWr9Th/YA5PT/raFxuu78F+fe3mBoDIAWwU9Q9J8dhsdxfXQg59N2GR6nmZ8v/1v4mqEx2j73DADmQ/81NI7z3XcB1Mn11Nn35i+LDY/TdmY0BZu3GR7H7Xf+nF2z3tAYLZ8cDcCpvyYZGqfNtMoVj4szr/wO0evVxLsX597ZaGgMgBZBo7iQsdfwOE19epPjO9TwOF13fUBRUZGhMVxdXQE4/5H9d9vWhmaPDADgTHGJoXHcm7gYfi1QeT2FW/9teJzmwx6j9Fie4XEa33F7nT1vpOFRMiciIiIiIvWWBhLap2RORERERETqLSVz9imZExERERGRekvvmbNPryYQERERERFpgNQzJyIiIiIi9ZaGWdqnZE5EREREROqtCuVydmmYpYiIiIiISAOknjkREREREam3KiwVN7oJ9ZaSuWqYzWZmzJgBQGlpKeHh4SxfvhxXV1cuXrzIggUL+Oqrrxg3bhxHjhzB2dmZXr16ER0dDcCSJUvYu3cvZrOZzp07s3r1au6//35iYmK49dZbKSoqIj4+nkaNGtGjRw+ys7Pp0KEDISEhrF27FgAfHx9mzpxJYGAgaWlpzJ07ly5duuDm5sbLL79MQkIC2dnZtGjRgp49ezJx4kROnDjB/fffz5dffkn79u2rXJeLiwu7du2id+/ezJgxgx9//JHk5GQWLVrE4cOHuXTpEv379yc8PJyAgAC6dOnCpUuXcHd358UXX6w2ZnX3zmw225Q5OzurC1hEREREfhFNmbNPyVw1VqxYwciRIxkyZAgAY8eOZfHixbRq1YojR44wb948Ro4cia+vL++99x533nknXbt2tZ7v6elJZmYmubm5DBgwAIDExERiYmLw8vKioKCA6OhoXnjhBYYPH868efNYtmyZ9fyMjAwCAwPZvHkzgYGBAAQHB2MymQgODrYel5CQgJeXl/VzSkoKixYtYuXKlcTGxla5rsGDB/POO+9w3333cfHiRQCysrL48ccfef311wGYNGkSQ4cOxdXVlcWLFwOwfPlyNm3aVG3Mn0tMTGTu3Lk2ZfHx8fxxwqSr3XYRERERkSq0AIp96jCpxsGDB3nooYesny0WC61atQKgS5cu5OfnA+Dn58eOHTtYt24dY8aMsR4fFBREamoqO3bsYODAgQDk5eVZkyA3NzfKysoAuPPOO2nevDlZWVnW81euXEloaCju7u7k5eUBkJqaytChQ7n33nutxyUkJGAymUhNTQVgz549hIWFsW/fvmqvq1mzZpSXl7NmzRoef/xxAL755hu8vb2tx/zmN78hJyfH5ryHHnqI7OzsamP+XFxcHAUFBTZbXFxctceKiIiIiDR0Z8+eJTQ0FDc3N9zc3AgNDeXcuXNXPCc/P5/Q0FDatWtH06ZN6dmzJ+vWratxbCVz1ejevbtNQuTg4MCZM2cAOHr0KLfddpu1vEOHDpSXl9O0aVPr8U2bNqWsrIyOHTvi4OAAQIcOHTh8+DAAhYWFODk5WY+fNWsWCxYsAKC4uJj09HTi4+M5duwYycnJAIwZM4atW7eSnZ1NaWkpUJlYJSUlMWbMGHbu3Mnx48cxmUx8//337Nq1q9prGzVqFEuXLrX2GHbv3p29e/da93/xxRfcddddNufs3bvXmoj+NGZ1nJ2dad68uc3m7Oxs916LiIiIiFxJhcVSK5tRxo4dy4EDB9i2bRvbtm3jwIEDhIaGXvGc0NBQDh06xKZNm8jKyuKJJ54gODiY/fv31yi2hllWY8KECcyYMYONGzdy6dIloqKimD59Oi1atKCoqIj58+fz9ddfA5WJmMViYfv27TZ1LFq0CAcHB5YvX249LjY2FldXV86dO8ecOXOsx16eg7ZlyxZSU1OZPXs2ISEhAIwYMYKHH34YAEdHR8aNG8eKFSuAysSqRYsWdO/enaysLNavX0+HDh344YcfSEhIwNfXt8q19e/fn927d3Pp0iUAevTowbZt25gyZQqXLl3Cx8eH9u3bU1RURHR0NBUVFbi7uzNhwgQ+//xzm5jTpk2r5TsvIiIiImKrPg+z/Oabb9i2bRsZGRn06dMHqJyy1bdvXw4dOsTdd99d7Xm7d+/mb3/7m3WE3OzZs3nllVf4/PPPefDBB685vpK5ari4uLB06VKbsn79+tl89vPzw8/Pz/rZ39+/2rpMJpP16zfffLPK/vnz5wMwffp0pk+fXmX/5blqgwYNAiAgIOCq7ffw8LAmkT91eXEVJycnnJycrL1+MTExVY7dsmVLlbKEhISrxhYRERERqY/sLdR3PaPIdu/ejZubmzWRg8qFDN3c3Pj000/tJnO+vr68/fbbBAQE0KJFC9555x3MZrNNfnEtlMzdpPLz80lKSrJ+HjZsmM3cOBERERGRhqC2eubsLdR3PR0W+fn5tG3btkp527ZtretsVOftt98mODiYVq1accstt9CkSRM2btyIp6dnjeIrmbtJtWvXTj1pIiIiItLgVdTSKMu4uLgqI+Hs9colJCRUSfx+bs+ePQDWNTJ+ymKxVFt+2ezZszl79iwffvghrVu35t1337Wug9GjR4+rXYqVkjkREREREbnp1WRIZVRUlHUNC3s6derEl19+ycmTJ6vsO3XqlHXRxJ/79ttvWbJkCV999ZV1pfoHHniAnTt3snTpUpvRdVejZE5EREREROqtG7EASuvWrWnduvVVj+vbty8FBQVkZmZapzR99tlnFBQUVFlz47Li4mIAGjWyfbGAo6MjFRUVNWqnXk0gIiIiIiL1VgWWWtmM0L17d/z9/ZkwYQIZGRlkZGQwYcIEhg8fbl385Pjx43h5eZGZmQmAl5cXd911F5MmTSIzM5Nvv/2WRYsWsX37duu7oK+Vg6U+r/UpIiIiIiK/amFL/1kr9aycOq5W6vm5M2fO8Mwzz1hXoR8xYgRLliyhRYsWQOV7qjt37szHH39sXa0yJyeHWbNmsWvXLs6fP89dd93FzJkzr/p+up9TMiciIiIiIvVWfU/mbiTNmZM6kdP/t4bW33XnvwAoyztuaByn2zsYHuNynKKiIsPjuLq6Un7qtOFxbmnTmrL8qpODa5NTu8pJxkbfN1dXVwDKjp8wNI5Th/aU/WB/SeNai+PRrs7i1NXP9M30MwB18zN9sz3XjP7eQN38N+rk0Q6ou+danfysGfy7ACp/H9wsz5vLcerqeVNfVdTWcpY3ISVzIiIiIiJSb2kgoX1aAEVERERERKQBUs+ciIiIiIjUWxplaZ+SORERERERqbc0zNI+JXMiIiIiIlJvWQx6R9zNQHPmREREREREGiD1zImIiIiISL1VoWGWdimZq4cyMzMJCgri0KFDTJo0idatW/OXv/yFPXv20L9/f7Zt28batWv5+OOPGTBgAHfccQd/+tOfbOp49dVXOXjwII6Ojjz00EM0atSIDRs20LJlS+655x5iY2OJiIjAyckJR0dHhgwZQlFRERs2bKBz584UFRXxpz/9CQcHB5KSknBzc+O7777jww8/ZPDgwQQGBjJ48OAbdIdERERE5NdCc+bsUzJXD61atYrZs2fz7rvvAnDhwgXMZjOpqakMHjwYPz8//Pz8iIiIYPHixbi4uNic/9VXX3H69GmWL18OQGlpKatXr2bKlCn4+/sTHBxsPfan5ycnJ1uP+fHHH5k+fTpz584FIC4uDoCQkBCSkpLstt1sNmM2m23KnJ2dr/OOiIiIiIjIz2nOXD1z8eJFzp49S3h4OJs2bQLg8ccfZ82aNVy6dIlmzZpdtY6DBw/Su3dv6+fGjRsD8Prrr9O3b1+GDRtm3RcdHY3JZOKjjz6yqaNVq1aUlpbWuP2JiYm4ubnZbImJiTWuR0REREQEKl9NUBvbzUjJXD2zbt06Tp48SXR0NFlZWRw7doyBAweyZMkSRo0adU11dO/enX379lk/X07KJk2axIcffsj27dut+xYvXkxSUhKPPPKITR1nzpyxJoE1ERcXR0FBgc12uVdPRERERKSmLBZLrWw3Iw2zrGc2btzI5s2bcXFxITMzEx8fHwB2796Nk5MTS5YsuWodPXr04N///jeTJk3illtuoVevXjRqVJm3N23alL59+7JlyxagsmfO0dERX19fAJYtW8a//vUvzp8/T0JCQo3b7+zsrGGVIiIiIiJ1QMlcPbNhwwbr197e3lRUVNjsX7t2rfXr5ORku/XMmDHD7r6pU6cCEBAQUGVfRERElbL58+dXG19ERERExGg3a69abVAydxPIzs62SbLGjh1Lt27dbmCLRERERERqh15NYJ+SuZuAl5fXLxoSKSIiIiJS3ymZs08LoIiIiIiIiDRA6pkTEREREZF6S3Pm7FMyJyIiIiIi9ZZyOfs0zFJERERERKQBcrCo31JEREREROop/z+/Xiv1bHt+Uq3UU59omKXUiaKiIkPrd3V1BeB82i5D4zTz86U4c5+hMQCaePciZ8Aww+N0Td9KyTeHDI/j0v1uTr2y1NAYbZ6tfH9i+anThsa5pU1rAP63aImhcdrOiKLkq28MjQHgcl93zv7zHcPjtBwXZPg9g8r7dnrZm4bGaD0lEsDw+9ZyXBAAJdmHDY3j4tWtzp5rdfW8Of9RuuFxmj0yoM5+t+X4DjU0TtddHwB187u6rp5r//vLYsPjtJ0ZbfjvHKj8vXN2daqhMVqOHWNo/ddLfU/2KZkTEREREZF6S68msE9z5kRERERERBog9cyJiIiIiEi9pWGW9imZExERERGReku5nH0aZikiIiIiItIAqWdORERERETqLS2AYp+SORERERERqbc0Z84+JXMiIiIiIlJvpSVE3egm1FuaM1dLkpOT2bZtm/XzSy+9xJNPPmn97OnpycaNGwEICQkBICAggOjoaKZMmcLs2bOtx77zzjv07dvX+jkgIICoqChGjhzJF198AcBdd92FyWTCZDLxww8/APDss88yffr0atuXkJBAYGAgAPn5+TRr1oyjR4+Sm5tLeHg4UVFRREZGcvr0adLS0hg0aBDTpk0jIiKCzz//3G7MnzObzRQWFtpsZrO5ZjdTRERERESuSj1zBtm/fz8dOnTghx9+wMPDg549e7Jq1Sp+97vfWY9xdXVl8eLFACxfvpxNmzYxYsQINm/ezOjRo/nss8/o06cPrq6uLFmyhN27d/Pxxx/zwAMP0Lt3b5KSkqx1lZSUUFBQYP3axcWlSpucnZ05ceIEb731FgEBAQDMnTuXl19+mVatWnHkyBHmzZvHyJEjCQ4OxmQyYTabefLJJ9mwYUOVmNVJTExk7ty5NmXx8fHMmDHjl91IERERERGplnrmDPDpp5/Ss2dPxo4dS0pKCgCOjo5ERkayfPnyas956KGHyM7OJi8vD3d3d8LCwli5ciUARUVFTJ06lUmTJll79fbu3WvtJbt48SIbNmxg2LBhDB8+3NoD+HOhoaGsWrWK3NxcOnfuDFT2pLVq1QqALl26kJ+fb3OOs7Mzzs7O1casTlxcHAUFBTZbXFxcTW6fiIiIiIhcA/XMGSA5OZni4mL++9//8vnnn1uTmYCAAEJCQigqKqpyzt69e/Hy8uIf//gH33//PXPmzOGTTz6huLgYV1dXli5dyqZNm9i6dStPP/10lV6yNWvW0LZtWwBOnTplM8Tzsk6dOrFixQpGjRrFV199BVQma2fOnMHd3Z2jR49y22232ZxTWlpKaWkpwDX1zP00+ft5PSIiIiIiUnuUzNWixYsXs379epKTkykrKwNg4cKFpKenW4+JiYmhT58+QGWPW3R0NBUVFbi7u/P73/+e119/nS1btgCVc+fWrVtnPXfEiBGMHj2acePGWXvJLtfZsWNHli1bBkBUVBRHjx6lU6dOVdr49ttv4+joaE0w58yZw/Tp02nRogVFRUXMnz+fr7/+mrfffptvvvmGwsJC/vjHPwJUienp6Vmbt09ERERERGpAyVwtiYiIICIiAoAVK1ZYy5977jkABgwYAECvXr0oLy8HsCZtP/XTsqCgIADCwsKsZevXrwfgv//9r815lxM5gCVLllSpNyEhwebz/PnzrV8nJyfb7PPz88PPz69KHT+PKSIiIiIiN46SuZvU2rVryc7OBqBJkybWpFJERERERG4OSuZuUpcXShERERERkZuTVrMUERERERFpgJTMiYiIiIiINEBK5kRERERERBogB4vFYrnRjRAREREREZGaUc+ciIiIiIhIA6TVLKVOFBUVGVq/q6srABcy9hoap6lPb04tft3QGABtoidR8nW24XFc7vXCnPOt4XGcu3pS+P4HhsZoPnwoAGX5Jw2N49TuNgAKNm01NI7biGGGx7gcx+jvDVR+f0qP5Rkep/Edt9fJcwCgcOu/DY3TfNhjAOT4DjU0TtddH3Dqr0mGxgBoM81ETv/fGh6n685/UZy5z/A4Tbx71dnvtpLsw4bGcfHqBtTN7+q6eq6VHT9heBynDu058/e3DI/j/vRTFGzeZmgMt9/5G1q/GEc9cyIiIiIiIg2QkjkREREREZEGSMmciIiIiIhIA6RkTkREREREpAFSMiciIiIiItIAKZkTERERERFpgJTMiYiIiIiINEBK5kRERERERBogJXPXISwsjBMnTlBSUkKbNm3o27cvJpOJmTNnWo/x8fFh3bp1AKSlpTFo0CAiIyOZPn26tSwoKIgpU6bwxhtvVIkRERHB5MmTCQ8P58033wQgJCTEuv/y1z//F+Do0aP06tWLadOmMX78eLZv317tdWRmZtKpUyfMZjMXLlzgySeftO5buHAh6enpfPzxx4wfP56oqCgmTZrEqVOnqq3LbDZTWFhos5nN5qvfTBERERERqZFbbnQDGrKFCxcSFxdH+/btiYuLo0mTJphMJuv+jIwMAgMD2bx5M4GBgQAEBwdjMpkIDg4GYOPGjbz44ot069bNbpxXXnkFgPHjxxMZGVmjNg4ZMoT58+djsVgYOXIkgwcPxsHBweaYVatWMXv2bN59912Cg4Np3rw5P/zwAx4eHuzatYuYmBhGjx7Nhg0brOdcunSp2niJiYnMnTvXpiw+Pp4ZM2bUqN0iIiIiInJl6pm7Du3ataN///5cunSJnj17snr1akwmE/PmzQNg5cqVhIaG4u7uTl5eHgCpqakMHTqUe++9F4DY2Fhef/11IiMjee+996qNExMTQ2BgIKNGjfrFbXVwcKBz585VetQuXrzI2bNnCQ8PZ9OmTQCEh4eTkpLCp59+St++fTl16hR33HEHAFu3bmXcuHHs2bOn2jhxcXEUFBTYbHFxcb+43SIiIiIiUj31zF2nLl26UFpaCsDYsWOtPXPFxcWkp6cTHx/PqVOnSE5OxtfXlzFjxjBhwgRCQ0MpLS3Fw8ODRYsWATB8+HBGjhxZJcZLL72Ei4sLQUFBBAUFUV5ebt1XVlZ2zW09evQobdq0sSlbt24dJ0+eJDo6mqysLI4dO0a/fv1YsGABR48eJT4+njZt2vD9998DMGzYMP73v/9x7ty5amM4Ozvj7OxcpfzyPRIRERERkdqhZK4WrV69mgMHDuDo6Ii3tzezZ8+2zmEbMWIEDz/8MACOjo6MGzeOFStW4OzszP79+6moqKB///7V1vvss89SUVFBjx49ABg0aBCTJ0+mtLSUp556yubY3Nxca0I5ffp0tm/fzsWLFykqKiIqKqrKEMuNGzeyefNmXFxcyMzMJDk5mTlz5uDr68uuXbvw8PAAICoqioiICNzc3Lhw4YJ620REREREbjAlc9fJz88PPz8/ANLT0+0ed3kI46BBgwAICAi4pvqTk5OrlE2dOrVK2dq1awH47LPPbMr37dt3xfp/Og/O29sbb29voHJoZ0xMjHXfoEGDrG0XEREREZEbT8lcPZOUlER+fj4AHh4eTJw4sVbr37ZtGxkZGdbPzz//PI0bN67VGCIiIiIiYjwlc/XMT1fDNIK/vz/+/v6GxhAREREREeNpNUsREREREZEGSMmciIiIiIhIA6RkTkREREREpAFysFgslhvdCBEREREREakZLYAidaIs/6Sh9Tu1uw2Aov/sMDSO66MDyXsm1tAYALe/toCLWV8bHufWHvdS8s0hw+O4dL+bs/98x9AYLccFAVBUVGRoHFdXVwDO/P0tQ+O4P/0UOb5DDY0B0HXXB5xJWWN4HPfwJw3/3kDl96d4z+eGxmjyUE8Afnz9H4bGaTVpPAAFm7cZGsftd/519lwr2LTV8DhuI4Zx/iP7rwqqLc0eGVBnz5uSr74xNI7Lfd2Bunl+1tVzra6eNyXZhw2P4+LVjdNJfzc0RmvT04bWL8bRMEsREREREZEGSMmciIiIiIhIA6RkTkREREREpAFSMiciIiIiItIAKZkTERERERFpgJTMiYiIiIiINEBK5kRERERERBogJXMiIiIiIiINkJK565CcnExAQAAmk4mePXuSnZ1t3bd//35CQ0OJjo5mypQplJSUYDabmTJlClFRUYSFhbF3714AunXrxuTJkxk9ejQXLlwgPz+foKAgJk+ezHPPPQeAl5cX69atA+Avf/kLQ4dWvnQzJCQEgFtuuYV9+/bZlP28rb179wbAbDbj4eFBWloaZ86cITIykqioKMLDwzly5AhHjx6lV69eTJs2jfHjx7N9+3YA+vTpg8lkwmQykZWVVe09MZvNFBYW2mxms/m677WIiIiIiNi65UY3oKGLjo7G39+fhIQEm/IXX3yRNWvW0LhxY3bs2MGyZcto3LgxI0eOZOjQoZSXlxMcHMz69evp2bMnf/vb30hMTCQnJ4fc3FwGDx7MxIkTrfU98MADfPzxxwQGBnLkyBFatmxpE2/o0KG89NJLrF271m5bPT09yczMJDc3lwEDBgCQmJhITEwMXl5eFBQUEB0dzQsvvMCQIUOYP38+FouFkSNHMnjwYDp37kxSUtIV70diYiJz5861KYuPj+ePpsnXcjtFREREROQaqWfuOi1evLjanqrGjRvTuHFjALy9vfn66685ePAgffr0ASp70lxcXLBYLOzfv5+wsDB27txJjx49CAgI4PTp00ycOJH58+cD4ODggIeHB+vWrcPHx6dKO1xdXenfvz9bt26129agoCBSU1PZsWMHAwcOBCAvLw8vLy8A3NzcKCsrsznHwcGBzp07c+rUKXJzc609c3l5edXGiIuLo6CgwGaLi4u7llspIiIiIiI1oJ6562SvZ66srIyysjKcnJzYu3cv99xzD40bN2bPnj0MGTKE8vJySkpKcHBw4MEHH2TlypXExcWRk5ODl5cXzz//PAAmk4njx48DEBoayoABAzh48GC1SdukSZMICgrC0dGx2rY2bdqUsrIyOnbsiIODAwAdOnTg8OHDdOvWjcLCQpycnKqcd/ToUdq0aXNNPXPOzs44Ozv/P/buPa7H+/8f+OOddPiYnMYmpznlPIeREL1DJJbltBxLS0I2YyHHMEtlZkpaW4bMoRJyijkLrQwjh3IanTZsVKu8O71+f/R7X9/3u3ozvK5XZc/77ebm6nrzer6v9/Xuuq7ndb1ez1eZ9QXl/FtCCCGEEELIq6NkjiNvb2/Url0bvXr1gpeXF1xdXVGnTh2oVCqsWbMGCoUCs2fPxv79+5GZmYl58+Zp/f/Zs2fD09MTrq6uCAsLg5GREfLz8/Huu+8CAJo2bYpbt26Vm3ABJU/7JkyYAE9PT53v8euvv4ZCoUBISAgAYP78+Zg3bx5q1qyJp0+fYsmSJQCAn3/+GXl5ecjOzoaHhwcUCoX0ZA4AXFxcYG5u/tqfGSGEEEIIIeTVUDL3GpydnaXl0k/mAGDz5s1l1gUFBZVZpx7nVr9+fWzatAkAYGlpWe6/USdy6p9L/z1ixAiMGDHiue8VgJSUAUBoaGiZf68upqLpl19+KbOOEEIIIYQQUjEomXtDxcTEIC4uTvp5wYIF0hg+QgghhBBCSNVHydwbytbWFra2thX9NgghhBBCCCEyoWqWhBBCCCGEEFIFUTJHCCGEEEIIIVUQJXOEEEIIIYQQUgUpGGOsot8EIYQQQgghhJCXQ0/mCCGEEEIIIaQKomqWRIiC9D9kbb+6acnE6tnHTskap+YAK6R+Ou/F//A1NV7ni7yr12SPY9ypA55duyl7HKMObfF32E5ZY9Sd+DEAoPDRY1nj6Nd/GwDwV2iYrHHqfTIRtywHyxoDAFrHHsbfm7bJHqeu8zhkZ2fLHqdmzZrITbgoa4z/9egGAPjrux9ljVNv6mQAQGb0QVnj1LK3Q6qHp6wxAKBxoL/s2wKUbM8/J2Nlj/OW0lL273TNmjUBAM8Sb8gax6hjOwAQsj23+g6RNQYAtD5zSNjx5tmNJNnjGLVrg8dBZecE5unt6Z/I2j6RDz2ZI4QQQgghhJAqiJI5QgghhBBCCKmCKJkjhBBCCCGEkCqIkjlCCCGEEEIIqYIomSOEEEIIIYSQKoiSOUIIIYQQQgipgiiZI4QQQgghhJAqiJI5QgghhBBCCKmCKJmTwaZNmzB00WET/QAAIABJREFU6FC4u7ujW7duuHnz/yZlvnTpEiZOnIiZM2di+vTpePbsGVQqFaZPnw4PDw9MmjQJFy5cAACYmZlh2rRpGDlyJHJycvDHH39gzJgxmDZtGubOnQsAaNu2LSIjIwEAq1evxuDBJZMMOzo6AgD09fXx66+/aq0rLT4+Hu+99x5UKhVycnIwduxY6TU/Pz+cPn0aJ06cwOTJk+Hh4YGpU6fi0aNH5balUqmQlZWl9UelUr3Ox0kIIYQQQggph35Fv4E31cyZM2Frawtvb2+t9V9++SW2b98OAwMDnDp1CkFBQTAwMMDw4cMxePBgFBYW4uOPP8auXbvQrVs3bNiwAT4+Prh16xbu3buHgQMHws3NTWqvc+fOOHHiBEaNGoW7d++iTp06WvEGDx4Mf39/7NixQ+d7DQsLw6JFi7Bnzx58/PHHMDExQXp6OkxNTREbGwtPT0+MHDkSUVFR0v8pKioqty0fHx8sW7ZMa93SpUux0M393350hBBCCCGEkH+BnszJJCAgAO7u7rh69arWegMDAxgYGAAAzM3Nce3aNVy/fh09e/YEUPIkzcjICIwxXLp0CZMmTcKZM2fQqVMnDB06FI8fP4abmxtWrVoFAFAoFDA1NUVkZCQsLCzKvI+aNWuib9++OHjwYLnvMy8vD0+ePIGTkxOio6MBAE5OTti8eTPOnTuHXr164dGjR2jatCkA4ODBgxg/fjwSEhLKbc/LywuZmZlaf7y8vF7hEySEEEIIIYQ8DyVzMpk5cyaCg4PRqVMnrfUFBQUoKCgAAFy4cAHt27dHu3btpOSosLAQz549g0KhQNeuXbFlyxZ07twZt27dgoGBARYsWICQkBD8/vvvSEtLAwBMnDgRX3zxBUaNGlXue5k6dSp++OGHcp+mRUZG4s8//8TMmTNx9epVPHjwAL1790ZcXBw2b94MJycn1K9fHykpKQAAOzs72NjY4OnTp+XGMjQ0hImJidYfQ0PDV/sQCSGEEEIIITpRN0sBvL29Ubt2bfTq1QteXl5wdXVFnTp1oFKpsGbNGigUCsyePRv79+9HZmYm5s2bp/X/Z8+eDU9PT7i6uiIsLAxGRkbIz8/Hu+++CwBo2rQpbt26herVq5cbX19fHxMmTICnp2eZ13bv3o19+/bByMgI8fHx2LRpE5YsWQJLS0vExsbC1NQUAODh4QFnZ2fUqlULOTk59LSNEEIIIYSQCkbJnAycnZ2l5dJj5gBg8+bNZdYFBQWVWace51a/fn1s2rQJAGBpaVnuv1EncuqfS/89YsQIjBgxokwMzXFw5ubmMDc3BwB4enpqJX/W1tawtrYu8/8JIYQQQgghFYOSuf+YmJgYxMXFST8vWLBAGsNHCCGEEEIIqToomfuPsbW1ha2tbUW/DUIIIYQQQshrogIohBBCCCGEEFIFUTJHCCGEEEIIIVUQJXOEEEIIIYQQUgUpGGOsot8EIYQQQgghhJCXQwVQiBDZ2dmytl+zZk0AQNahn2WNYzLEBgWpabLGAIDqjRvh0doNssepP2sant1Ikj2OUbs2+GO5r6wx3l1SMj9jwR9/yhqn+rvvAICQ7ZH79wYo+d0R9V271c9O9jitTx/EH96rZI3xrvd8AMCTbRGyxqkzbjQAoCAtQ9Y41Rs1REH6H7LGAIDqpu8i/0Gq7HEMmjZG1sEjsscxsRsk7Nz2cHWArHEafDETgJhztajj2i3LwbLHaR17GIWPHsseR7/+23gavlvWGLXHOMjaPpEPdbMkhBBCCCGEkCqIkjlCCCGEEEIIqYIomSOEEEIIIYSQKoiSOUIIIYQQQgipgiiZI4QQQgghhJAqiJI5QgghhBBCCKmCKJkjhBBCCCGEkCqIkjlCCCGEEEIIqYJo0vBXtGnTJkRERKBJkyZo1qwZvvrqK9y8eRONGjWCo6Mj+vfvj4sXL+Lo0aMYOHAgrK2t8c0336Bbt25ITU3Fhg0b0KhRI6hUKrRp0waRkZHo3r07Nm3ahICAACQkJCA5ORk7duyAt7c3FixYgKdPn6K4uBh2dna4ePEibt68idq1a6Nbt26wt7fHp59+inr16qFmzZrw8/Mr834DAwNx4cIFqFQqNG/eHNu2bcP7778PT09PGBsbIzs7G0uXLoWenh5GjhwJS0tLZGVlYdy4cbCxsUHPnj3RtWtXAMCMGTPQqVOnMp+LSqWCSqXSWmdoaCjfjiCEEEIIIeQ/ipK51zBz5kzY2tri999/x5UrV7By5UoEBQUBANzc3AAAjo6OCA4OBgDs3r0bQUFB2L59OxISEtCoUSPs3r0bixcvRlhYGLp37w4A6NSpE7Zu3Qpzc3MAwP79+9G8eXNMmTIFAJCfn4+LFy/C29sbbdu2ldoeOHCgFLc8LVu2RHx8PO7du4d+/foBAHx8fODp6Ym2bdsiMzMTM2fOxPLly2FjY4NVq1aBMYbhw4dj4MCBaN68ubQtuvj4+GDZsmVa65YuXYo5c+a81GdLCCGEEEIIeT5K5l5DQEAA9uzZgy5duqBZs2YAgKtXr+r89/fv38eUKVNw7do1HDlyBACwZ88ebNmyBa6ursjLywMAfPzxxwgNDcX7778PALh+/ToGDRoktWNgYAAA8Pb2Ru3atTFgwAAMHz4cq1evhpubG1q0aIH58+eXiT9mzBhEREQgJycHVlZWAIDU1FQpIaxVqxYKCgq0/o9CoUDz5s3x6NEj3Lt3D+7u7gCARYsWoXHjxmVieHl5Yfbs2VrrDA0NkZ+fr/NzIYQQQgghhLw8SuZeg+aTueDgYMyfPx8eHh46/32zZs3w/fffIyAgAL/88gtat26Na9eu4dNPP8Uff/yBXbt2AShJoGbOnIl169ahadOmaNeuHX799Vd06dIFAKTESPPJHAAsWLAAAODu7o60tDQ0atRIK36NGjVQUFCAJk2aQKFQAAAaNWqE5ORkmJmZISsrC9WrVy/zvn///XfUr1//Xz2ZMzQ0LLdbJSVzhBBCCCGE8EXJ3GtQP5nLzc2FqampNH7twIEDz/1/U6dOhaOjIzp37ozvv/8eFhYWePbsGSZMmIBhw4YBAKysrLB27Vo0bdoUw4YNg5eXF6ZPnw4AsLW1BfB/T+batWuHDz74AGFhYTAyMkJ+fj7efffdcmN//fXXUCgUCAkJAQDMnz8f8+bNQ82aNfH06VMsWbIEAPDzzz8jLy8P2dnZ8PDwgEKh0Hoy5+LiInUDJYQQQgghhIhHydwrcnZ2hrOzc5n1s2fP1upmuGPHjjLLBgYGiIqK0vp/RkZGiIyM1Fq3e/duaXnVqlVar9nb25eJbWlp+dz3q0mdlAFAaGhomX//66+/lln3yy+/6GyfEEIIIYQQIhYlc2+omJgYxMXFST8vWLBAGmtHCCGEEEIIqfoomXtD2draSt0xCSGEEEIIIW8emjScEEIIIYQQQqogSuYIIYQQQgghpAqiZI4QQgghhBBCqiBK5gghhBBCCCGkClIwxlhFvwlCCCGEEEIIIS+HqlkSIQrSMmRtv3qjhgCAh37rZI3TYO6n+MN71Yv/4Wt613s+Hq4OkD1Ogy9mIuvQz7LHMRligztDRskao+Whknka8+/+LmscgxbvAQBuWQ6WNU7r2MP4KzRM1hgAUO+Tibg3fJzscZrv3YY/lvrIHufdZV5Im7NI1hiNvv4SAPD7mMmyxnkv/EcAQGb0QVnj1LK3E3Zck3tbgJLtkftcAJScD7Kzs2WNUbNmTQDAo2/Wyxqn/uczAEDI9og6rv2xwk/2OO8unov8B6myxzFo2hj3Rk2SNUbzyC2ytk/kQ90sCSGEEEIIIaQKomSOEEIIIYQQQqogSuYIIYQQQgghpAqiZI4QQgghhBBCqiBK5gghhBBCCCGkCqJkjhBCCCGEEEKqIErmCCGEEEIIIaQKomSOEEIIIYQQQqogSuY427RpE2JiYgAAzs7OyMnJwdixY6XX/fz8cPr0aTg6OgIAWrZsid27dwOAtO63336Ds7MzPDw84O7ujps3bwIA4uPj8d5770GlUkntT5s2DSNGjMCRI0cAAD179oS7uzvc3d1x9epVAIC/v7/WeyhNs11d7/fEiROYPHkyPDw8MHXqVDx69KjctlQqFbKysrT+qN8vIYQQQgghhB9K5mRWrVo1mJiYID09HQAQGxuLfv36Sa9369YNYWFhKCwslNatXLkSISEhCAwMRHBwMFq1agUACAsLw6JFi7Bnzx7p337zzTdYv349Dh48CABo3rw5goODERwcjE6dOgEALl26hEaNGknvoTTNdmvUqFHm/fbt2xcBAQH48ccfERgYiO+++w5169Ytty0fHx/UqlVL64+Pj8+rfnyEEEIIIYQQHSiZE8DJyQmbN2/GuXPn0KtXL63XqlWrhk8++QQhISHSOn19fRgYGCAuLg4uLi7Yt28f8vLy8OTJEzg5OSE6Olr6t3PmzMGAAQPg5uYGALh37570ZC41NRXnzp1Dt27dMG7cOGzevLnMeyuv3dLv99GjR2jatCkA4ODBgxg/fjwSEhLK3VYvLy9kZmZq/fHy8nq9D5AQQgghhBBShn5Fv4H/gt69e8PX1xe///47li5dWub1oUOHwtHREdnZ2QCAwsJC5Ofnw8LCAs+ePcPNmzcRGRmJP//8EzNnzsTVq1fx4MEDAMDXX3+NmzdvIjw8HN7e3tKTObXly5cjNzcXt2/fxsWLF8skVuW1W/r91q9fHykpKQAAOzs7PHz4EE+fPi13Ww0NDWFoaFhmfcGrfXSEEEIIIYQQHSiZk0FAQAD27NmDqKgoKbGytLREbGwsTE1Ny/0/np6e6NmzJwBg4cKFcHNzg4mJCfLy8jBlyhSsWrUK+/btg5GREeLj47Fp0ybp/3bp0gXffvstHj58KD2ZA4CxY8fiyZMniIiIAPB/4980u3nu3r27TLtLliwp8349PDzg7OyMWrVqIScnh562EUIIIYQQUsEomePM2dkZzs7OAKD1hMzT0xOenp7Szzt27ND6+4MPPpDGzXXu3FkrWQOAqKgoadnc3Bzm5uZar//4448AgF9++UVrvZWVlbQ8d+7cMu9XV7ul36+1tTWsra3L22RCCCGEEEJIBaBk7j8mJiYGcXFx0s8LFiyAgYFBBb4jQgghhBBCyKugZO4/xtbWFra2thX9NgghhBBCCCGviapZEkIIIYQQQkgVRMkcIYQQQgghhFRBlMwRQgghhBBCSBWkYIyxin4ThBBCCCGEEEJeDj2ZI5WOSqWCt7c3VCoVxalEMSgOxREVg+JQHFExKA7FERXjTYxDKgd6MkcqnaysLNSqVQuZmZkwMTGhOJUkBsWhOKJiUByKIyoGxaE4omK8iXFI5UBP5gghhBBCCCGkCqJkjhBCCCGEEEKqIErmCCGEEEIIIaQKqubt7e1d0W+CkNKqVasGpVIJfX19ilOJYlAciiMqBsWhOKJiUByKIyrGmxiHVDwqgEIIIYQQQgghVRB1sySEEEIIIYSQKoiSOUIIIYQQQgipgiiZI4QQQgghhJAqiJI5QgghhBBCCKmCqMQNIYQQUkUFBQXpfG369OkC3wkfbm5uCAkJAQAsWbIEy5cvBwCMGzcO27Ztq8i3Rsgryc3N1fna//73P4HvhLypKJkjFS4oKEi66IiKisKIESMAAH5+fpg7dy7XWAcPHtT5mp2dHbc4mhchmts3e/ZsrFmzhlscT09PKBSKcl/z8/PjFkeE69ev63ytffv2At8JH2/SvgHerKRB1HdN83h27tw59O7dGwCwZcsWTJo0iUuMGjVqcGnn3xBx/Lx165a0HBsbKy1nZGRwaV9N1DFa5PntTZKbm1tuovPw4UM0aNCgSsUZOnQoFAoFShePVygUOH78OJcYACWN/2WUzJEKFxkZKZ3sAgMDpZNdTEwM95NdQkJCuesVCgXXZE7zIkRz+y5fvswtBgAMGzaMa3u69OjRQ+fJKD4+nksMf3//ctcrFAps3LiRSwxAzLYA4vbN6NGjyySNjDEoFAqEh4dziyMqaRCxPaK+a5rHs0WLFkkXbps2beKWzDk5OQEA0tLSkJ6eDlNTUzRq1IhL26WJOH7qugGia/2rEnWMFnV+s7e3R3R0NADtp5v9+/fnmjCIijNs2DCpPc2Yjo6OVS7OiRMnuLTzIqKSRlL5UDJH/lOWLl0KoOTi8PHjx3j77be5XySIZGVlhfz8fCQkJEgXcj169ICBgQHXOLou4nj68ccfZY8BiNkWQNy+Wb16Ndf2dBGVNIjYHlHfNRFSUlLg6OiI6tWro2HDhkhPT0dRURF27NiBxo0bc40l4vh54cIFmJubgzGGpKQkaTk5OZlrnDdNdna2tKz5dJP3VMKi4mi2pxmzKsZhjCEkJASRkZHSsXPMmDFwdXXl+vsjKmkklQ8lc6TC3b59G3PnzgVjTGv5zp073GM9ffoU7u7uuHjxIt5++208fvwY3bt3x/r161GnTh1ucRITEzFmzBgwxrSWr127xi0GAFy7dg1jxoxBjx490LBhQ+zfvx/u7u7YuXMn966Jhw8f1joZjR49GoMGDeLWvkqlgre3N/bu3QuVSgVDQ0M4ODhgyZIlMDQ05BYHKPnc9uzZI23LiBEj0K5dO+4xROybZs2a4dGjRzhy5Ii0PYMGDUL9+vW5xQDEJQ0itkfUdy01NRVBQUFgjGktp6WlcYsxd+5cLFu2DAMHDpTWHTt2DJ6enti+fTu3OICY42dWVhaXdl5E1DFa1PlNMynQtVyV4hQXFyMvLw/FxcVllqtaHD8/PyQlJSEkJASmpqZIS0vDV199hb///hvz5s3jFkdU0kgqHwXjfZuDkJd06tQpna9ZWVlxjTV16lSYm5vjk08+kdb9+OOPOH/+vNRdhIf79+/rfK1Zs2bc4gwfPhxffvklOnXqJK1LTEzEwoULsXfvXm5xQkNDERERgXnz5sHU1BTp6enw9/fHyJEjtT7L1zF//nwYGBhg0aJFMDAwQH5+Pnx8fJCbmwtfX18uMQAgOjoay5cvx4wZM6Rt2bBhAxYvXowPP/yQWxxR++bs2bOYMmUKxowZIyVZUVFR+O6776QxWjyMHTsWn3zySZmk4YcffuCaNIjYHlHftc2bN+t8Tf2k83UNGDAAx44d+9frX4eo46eImy2ijtGizm9mZmaoXbs2GGPIzMyUlrOyspCUlFTl4lhbWwvpMigijlKpxPHjx6Gn938F5AsLCzFgwIDnfj9elq+vL5KSkrB48WKtpLF169Zck0ZSCTFCKti1a9dYYmKi1rqrV6+yGzducI/Vv3//l1r/Oh4+fMi2bt3K/Pz82NatW9nDhw+5xxgwYMBLrX9VVlZWLDc3V2tdbm4us7Ky4hZDqVS+1PpXZW1tzf766y+tdX/99ReztrbmGkfUvhk8eDC7f/++1roHDx6wwYMHc40j6ndHxPaI+q6ppaamsvj4eJaamsq9bZHHNBGx9u7dyz744AO2ceNGFhMTwzZu3Mh69OjBoqOjucVQE3GM/q/47bffKvotVFoiz9NFRUVa6woKCli/fv24xiGVD3WzJBXu008/xc6dO7XWNWzYEGPHjsWRI0e4xhI1uL7004Xk5GSsWrWK+9OSwsJC5OXlad1VZIyhsLCQWwwA0NfXh7GxsdY6Y2Nj6OvzO4RUq1btpda/Kj09PdStW1drXd26dbnHEbVvioqK0LRpU611TZo0QVFREdc4oojYHlHfNRFdU1NSUmBubg7g/wrFsP//1IQ3EcfPtWvX4siRI1q/o8OHD8eoUaO4PjkXdYzu1auXtE/Un9Pjx49x584d7r+jcneFf54vvviC6/maCeoyKCJOcnJymWI3jDGtMYc86Ovraz39U6+rXr061zik8qFkjlS4goIC1KtXT2tdvXr1kJ+fzz1WfHx8uRc+vAfXr1ixAjExMVoXpa6urpgyZQpiYmK4xVEoFBg6dKiUMJS+aODlwYMHZUrTM8aQkpLCLcaVK1cwZsyYMjGuXr3KLQZQUnK6dIl1xhgePnzINY6ofZOZmVmm1D77/92eeBKVNIjYHlHfNRHj2UQWBhFx/BR1s0XUMfr8+fPS8u+//47Vq1fjt99+w5dffsktBqC7K3xKSgq3rvDPwxjfETuixpmJiBMWFlbuet4Vj0UljaTyoWSOVDh9fX1kZmaiVq1a0ronT55wP3kD4gbXi3paIqp61cKFC8tdv2DBAm4xRFWZHDlyZLmx1CXDeRG1bzp06FBuqX3eBXBEJQ0itkfUd+3hw4daiRxQMpbtq6++4hZD1HxpgJjjp6ibLSKfaF+9ehW+vr74+++/MWfOHAQGBnKPERYWhkOHDkk9KNq0aQMLCwsMGTKEazJX3lyDjDH89ddf3GIAwKFDh7TGmbVo0QLBwcEYMGAA12RORJzq1atLT3vv3r2LFi1aAAD3nkeikkZS+VAyRyqct7c37OzsMG7cOJiamiI1NRU7d+7kWohATdQErqKelmjO86N5UTdu3Dhs27aNW5y+fftKJyDNSVavXLnCLcavv/4q+wTLQMlk3romieVJ1L4RVWZfVNIgYntEfddE0DVf2qVLl7jHEnH8FHWzRdQx2s7ODmlpafD09ETXrl2hUCikuDxvUIjoCg/ovhEyfPhwrnFEdRkUEUdzjklXV1dpedWqVVy7wYpKGknlQ8kcqXB9+/bFvn37cODAAdy6dQumpqbYt28f16kC1ERN4CrqaYlm9wnNi7qMjAyucTRPQJqTrM6aNYtbxS8REywD4iajFbVvRE3iKyppELE9or5r6q6ppbugiXjCJUcpchHHT1FzgYo6Rr/zzjt45513cOzYMa3qorwnqBfRFR4oGRuXn5+vdX5+8uQJ9+ljRHUZFBGn9LhpuYhKGknlQ8kcqXDqu5QffPCBtC4jIwMZGRncT6yiiHpaIqqgi66TkZwnJrlovmc5J6MVtW9ETeKry5u2PTyJ6Joqar40UTIzM+Hh4YG4uDg0aNAAjx49Qq9evbBu3TqtrvivS9QxWlQcEV3hAcDFxQULFizQSuZSUlLg4+PDdYoSUV0GRcT566+/cPDgQak7quYyT1X93ExeHSVzpMKVd3cU4H/nEhA3gWt+fj5WrFiByMhIFBQUwMDAAKNHj8bChQthYGDALc6FCxekO/9JSUnSMu+LyJycHNy4cQPFxcXIzc3VWuZFxATLgLjJaEXtG1GT+IpKGkRsj6jvGgAcP34c27dvlyrljR8/Hkqlklv7osb/AWKOn9OmTYOdnZ3WRfa2bdvg7u7ONVkQdYwWFUc9b2FaWpr0XWvUqBG39tUyMjLQuXNnrXXvv/8+0tPTucaxsrJCUVER4uLipO2xsLDgPpZeRBzNrsOay7y7DotKGknlQ5OGk0pLfWDlSdQErtOnT4epqSk8PT1haGgIlUqFNWvW4MGDB9iwYQO3OKJMnjxZ52u87jyLmGAZ0D1JLCCuaAlPoibxFTXJsojtEfVdCwgIwKFDhzBnzhw0adIEqampWLNmDWxtbeHh4cElRulxX5p492wQcfxUKpU4efLkv17/qkQdo0XFSUtLw7hx48AYQ+PGjZGamgo9PT1s3bqV2zQYQMl+Lv09KC4uhlKpxOnTp7nFSUxMxLhx49ClSxdpe65cuYJt27Zx/V6LiiPCsmXLdL6m7r5M3kyUzJEKl5WVhZ9++gmMMUycOBGpqalYtmwZMjIynnvx8Cqe9ySpvKIYr6q8Ex7A/4KkvMpianZ2dtzikJf3pu0bkUnDm8LS0hInT57UKkJRWFgIpVKpNQbxdejp6cHc3FzaB5pTYfDu2SDi+KlUKhEdHQ0TExNpXVZWFuzt7bkeO0Udo0XF+fDDDzF37lz07dtXWhcbGwtfX1/s27ePW5zg4GCcO3dOmgIhLS0Nfn5+6N27N9zd3bnFGThwINavX482bdpI65KTkzF9+nQcPXq0SsVRT+ehST21R3x8PJcY5L+NulmSCjd69GgolUo8ffoUSqUS7dq1w8KFC9GpUyfusd566y106dIFtWvXltapD6o8i0WoJ4fWvIjLz8/n3o99+PDh+OCDDzBw4EAYGBhoXcjxTBhKz8mlKTw8nEuMBg0aoGnTplqVxeQ44T2vUIOfnx+3OKL2TemiB5rUxSp46Nixo5CkQcT2iPqu6evrl6kmWN661xEXF4ddu3bh+vXr6Nq1K0aNGoX333+fW/uaRBw/vb29MWjQIIwfP156UrJjxw74+PhwaV9N1DFaVJx//vlHK5EDSm4mLF68mGscd3d3tGrVCmvWrEFGRgYaNWoEJycnDBgwgGucwsJCrQQLKHlqX1hYWOXi9OjRA2lpaejTpw9GjhwpVZnkjZLG/y5K5kiFKygogJeXF4CSC8YtW7aUKRXMy44dO7Bv3z7k5+dj8ODB+Oijj8pMUMvDzJkzYWtri88++0y6IAkMDMTMmTO5xklNTUVUVBSOHj0KExMTODg4wNbWlutYDKBkzFx2djbs7e1hZ2eHGjVqcG0fKKmSdu7cOTRu3BgjRoyAlZWVLHMNHjx4EDVq1MDo0aPRo0cP7u2rido3ixYtgpmZGRwcHNCwYUPZBr6LShpEbI+o71rfvn3h5eUFLy8vmJiYIDs7G6tWrYKlpSW3GObm5tJFXFxcHJydnWFubo7g4GBuMdREHD+VSiUOHTqE/fv3S9WN9+/fz726sahjtKg41atXR1pamtY4ufT0dO6l/IGSp1ma8ycWFRUhPDz8uTf9XpaJiQkSExPRsWNHaV1iYqLWE9uqEmf9+vVgjCE2NhYBAQG4c+cOxo4di7Fjx3KLAYhLGknlQ90sSYUzMzPDrFmzwBjDt99+i1mzZkmv8XyyoOnZs2c4dOiQVLJ3xYoV3GMkJydj586dyMjIgKmpKcaMGQMzMzPucdQuX74MFxcXdO7cWZYKapmZmYiOjsbBgwehUCjg4uJSZkJkHlJSUrBr1y7l/p67AAAgAElEQVTs27cPw4cPx6effso9xs2bNxEREYGEhAS0bt0akyZNKjOonyc5901BQQGOHj2K3bt34/Hjx1AqlRg1ahT38aaa4uLiMH36dFmSBpHbI/d3jTGG0NBQ7NixA9nZ2TAxMYGjoyNcXFy4FXTJy8vDoUOHEB0djby8PNjY2MDBwQH16tXj0n555Dx+iuzOK+oYLSLO1atXMWXKFPTp00dKGs+fP4/vv/8eHTp04Bbn0qVL0lPSJUuW4Pz58wgODoatrS1WrlzJLU5qaiqcnJxQu3ZtaXuysrKwceNGNGnSpMrFKSoqwrFjx7B79278+eefmDhxIhwcHLi1r6ZOGqOiomRLGknlQ8kcqXCiihGoXbx4Ebt27UJiYiI6deqEiRMnlulm8bpEjc27f/8+du3ahdjYWDRs2BAjRoyAUqmU5SkDUHJRsmvXLpw8eRIjR46Em5sb1/YfPXqE3bt34+jRo6hRowY++eQTrk8xNGVnZ2P//v0IDAyEnZ2dztLer0r0vgGA06dP4/PPP0ffvn2xdu1arm1XRNIg5/aI/K7J6a233kKHDh1gb2+P2rVrayWJctwMk/v4KWoMoKhjtEiFhYU4f/68lDRaWFhwnzS8d+/e+Prrr/HkyRM4Oztj8eLFmDZtGvc4aikpKdL28CzkIjKOi4sL0tPTMWDAADg4OGjdmOL9XROVNJLKhZI5UuEKCgoQERGBtLQ0tGnTBvb29rLFatGiBerXr49Ro0ahffv2Whc+PMcx6enpCRmbV61aNXTv3h2WlpbQ09PT2h6e47+WLVuGS5cuoU2bNhg5cmS5ffNfl7W1NVQqFRwcHGBjY6PVHZHnHfnNmzcjJiYG1atXx7BhwzB06FBZuo2K2jenTp1CVFQUHjx4gF69emHkyJFo2bIlt/bVRCUNIrZH1HetR48eZZ7A8R7DIvJmmIjjZ3x8vJDuvKKO0b169ZKq5964cQPt27eX4pw7d45bHFEFl/r37y99PhYWFoiLi+PWtiZRY4FFxLG2tpaW1b8zcnzXRCaNpHKhZI5UOAcHBzRt2hQdOnTAmTNnUK9ePe534dV0le5VKBRYsmQJtzjh4eFCxuaJmmqhRYsWWk9g1BcnPC9KS09/oBmD5x15PT09tG/fHsbGxmXi8BwkLmrf6OnpoXv37tL0AJoX2LyK0wDikgYR2yPquzZjxowKH8Mybtw4bNu2jUtboo6fanJ25xV1jNZkbW0t2/Qn1atX11lwiee+adCgAZRKJRhjOHXqlLSsUCi4Hm/q1q2rc+wsz+ONqDjPs2XLFkyaNOm12xGVNJLKh5I5UuFKl2jmXbL5ZcyePRtr1qzh1p6IsXnPo3kXVU5HjhzBoEGDZI3h5+f33EqUvFy5ckW2aoCaeO2bF83/9vDhQzRo0OC147wIr6ShMmwPz+9aRY9hkTOBKI3H8VN0d16Rx2g5j8d//vmnkIJLouabFDV2tiLGHJcm6jzNK2kklQ9VsyQVLj09Xaurg+bPchVA0eXy5cvc2tIcW2JjY4MJEyZwa/vfEnWvRn0hJKeYmBghydysWbOEnFh57ZsXXUA5OjoK2Z6MjAwu7VSG7eH5XSsuLkZeXh6ePXsGfX19GBkZcWn33+JVaOXf4HH8rF+/vlZ33vz8fOzcuRMA//OBiGO0uqALYww5OTlaBV54dul95513MG3aNEybNk0quLR7927uBZdycnK4tqdL9erVMWTIEAwZMkQaO3v37l3uvXZExXkeUefpTZs2UTL3hqJkjlS40oUneBeiqAiaY0vc3NygUChw584d3LlzR+iE0aIu5N6kB/yituVN2zdv2vbwoDmGZc6cOdLd/tzcXGFjWKrS5wWUlHEXQdQx2t/fX1pu37699DPvLr2lCy75+/tDqVRya19Nc3s08d6e0mNnw8PDZRkLLCrO89Cxk7wuSuZIhXtRv3SeYz5ehNfBTr1Nubm5uHDhgrSe94TRL/ImXci/SdsC0Pa8qqr0Xbt37x6AkuIUhw4dktqWYwwLYwyPHz/G22+/rfUZTZs2jWucF72H1yXqfCDqGP3jjz8iLS0N6enpZcYe89SiRQutgkuHDx/G4cOHAfAtuCTHtDflsba2lsbOJiQkaO0jnmPzRMV5nvfee09IHJFP6YlYlMyRSo9X963n+eWXX9CzZ09uJXyXLl363Nd5j83TdPfuXURERGDevHk6CxbwJqLrWK9evWSPAciblFTEvpFre9Sl3dVPmEQlDXLun4yMDDRs2JDbd+1FY9V4jGF58uQJpk2bhosXL6JevXp4/PgxevTogfXr16NOnTpcJ3LWRaVSwdDQUEgJdF7nA1HH6GXLliE6Ohpt2rTB1atXMXv27DIFeHgQVeDC1dUV48eP1yq2cfLkSWzbtg0hISHc4qhvhOjCa+ysiDhbtmzR+dqkSZO4PtF8HlFJI6kAjJBKztraWpZ24+Li2Jw5c5hSqWRTp06VJYYuvLfpzp07zMfHh1laWjJXV1e2bds2ru2XdvXqVbZ48WLWvXt3bm1+//337OnTp4wxxuLj41m/fv2YlZUVO3XqFLcYjDGWmprKVCoVY4yxvLw8FhISwoKDg1lOTg5jjLHMzEyu8UTvm9KSkpK4tDN16lRp/4SGhrKuXbuy3r17s8DAQC7tq+Xn52v9fOTIERYTE8OKi4sZY/y2Ry09PZ0FBASwwYMHsyFDhnBt+0V4HAfc3NzYDz/8oLVu48aNbMqUKa/ddmkDBgxgly9f1loXHh7OzM3NucfSRa7zgVxxLCwspO9ubm4us7Ky4tLuy+K1PX369Cl3fd++fbm0/29Vpe9BcHCw9Oe7775j33zzDevUqROrX78+h3f4f/bv3y+dv27fvs3Gjh3LHB0d2bVr17jGIZUPPZkjlR7jeCc+Li4OERERuHLlCvr06YOEhITnlpCv7FatWoVjx46hZcuWcHR0xIkTJ/D999/LEuvq1asIDw9HQkICbt++jc2bN3MteR0aGgpXV1cAwJQpUxAZGYm6deti+PDhOHPmDLc4o0ePlp6YuLi4wMzMDA0aNMCECRMQFRUFExMTLnFE7RvNeazU3WgeP36MO3fuoKioCGZmZlziXLt2DbVq1QIA+Pj44PLly/jf//4HS0tLzJgxg0sMoKSy28GDB1GzZk3MnDkTOTk5ePvttxEeHo7Q0FAu25ORkYGIiAgcPnwY77zzDhITExEbG8u98t+L8Di23b59G999953WusmTJ2Pr1q2v3XZpISEhmDt3LurVqwdHR0f4+PigS5cu+Pnnn7nH0oXn+UCEoqIi3Lx5U3rfubm5UhEUngVQXoTX56ZrcnDRXfhEfQ94xJk6dSoA4OnTpwgMDER0dDSmTJkine94WbFihTTvn5OTE1auXIm3334bHh4eNDXBG46SOVLpyNl9q3///nBxcUFkZCRq1arFdV6xl8HrRLRv3z40a9YMw4cPh6WlJapVq8al3dLatm2L3r17w83NDStWrMCQIUPQp08frjHUF9IpKSkwNjZGq1atAOi+eHidOIaGhsjNzUVCQoI0/iYyMpJrHFH75vz589Ly77//jtWrV+O3337Dl19+yTVOYWEhgJKKhWZmZtJE67y3S09PDzVr1kR+fj4OHjyIO3fuANCeQ+l1NWnSBO7u7oiMjISxsTGGDBkiPJED+FwA62pDjovrFi1aYMOGDbC3t8eOHTuwcOFC2SrMsgoeA8jrGN2hQwetMWsdOnSAv78/94IhL8Lr+9C/f38sXLgQXl5eeOutt/DPP//gq6++woABA7i0/2+JSh55xElPT8eaNWukeRPPnz8vy/lAPXfqw4cPkZOTw3U+U1K5UTJHKpy7uzt8fX1Rq1YtbNy4EYGBgTA2Nsa4ceMwY8YMrmM+/vzzT0RHR8PNzQ3FxcVIT0/HkydPUKdOHW4xnof32JKzZ88iNTUVkZGRCAoKwrVr17B3715YWVmhdu3aXGIAwOeff44DBw4gICAA6enpKCoq4ta2WrNmzTBv3jxcuXJFGkeUm5uL/Px8rnEYY7hx4wZOnDgBGxsbaT3vktui9g1Q8tTU19cXf//9N+bMmYPAwECu7QPAxx9/jF69euHvv//Ghg0bAACpqanck6CCggJkZWXh+PHjWjcMnj17xi3G9evXERERAQcHB7Rp0wZPnz7l1vbL4DGGJT4+Hubm5lrrGGNITk5+7bZLW7t2LSIjI7FixQr07NkTy5cvh62tLfz8/LjNzVjRYwB5j59+UcEQUfNn8kpOFy9ejNDQUHz00UfIzs5GrVq14OjoKMs4wOepSk/mmjdvjjZt2mDw4MG4dOmS1hQePIvT1KxZExs2bEB8fLz0e1JQUCDdICdvMPE9OwnRZmlpKS23atWK/fPPP6y4uJj17t1b1rjZ2dnsp59+YiNGjGC9evXi2nZFjS1JTU1la9euZf369ZOl/cePH7OQkBA2dOhQNmrUKPbtt99yazspKYkdOHCAnThxQlqXkZHB4uPjucVgjLErV66w0aNHM2dnZ/bo0SPGGGO3bt1iK1as4BqnNLn2zZAhQ9j777/PwsLCWGJiIrt27Zr0h6fw8HCWnZ0tjS1kjLFnz56x7OxsrnGOHTvGevfuzZRKJbt79y5jrGT/uLu7c42jlpyczFauXMmsra2Zg4MD17YrcgzLn3/+yb3NdevWscLCQq11SUlJzN7enlsMkWMA1d6k8dOa7ty5w1atWsUYY9zHHuvy+eefC4nDa+ysiDG6J0+e1PmHp6ysLLZ+/Xr2448/Sr+n9+/fZ/v37+cah1Q+lMyRCmdhYcEYY+zSpUvMzs5OWi/HgOo7d+5Iy5oXpWfOnOEeZ+TIkczNzY0dP36c2djYME9PT+7FNXbt2iUtnz17VlretGkT1zjlefz4cZmLrtchakC7i4uLkDii9o2TkxNzdnYu82fy5Mlc44jaP5q/l5p4Jie6YvA+DvTs2VNa7tOnDzt58iRLTEwU8lna2NjIHoOx/yvww0v//v1fav2rOn/+PJs9ezYbOHAgW7p0qWw3wF6kqhfDKo1n4ZhevXpJf/fq1Yu1bt2a6enpcWlfzdLSkmVlZTHGGPPw8GCTJ09mnp6e3M8ThYWFLDY2loWHh7PY2NgyN0Xkcv36dTZt2jQhsUjFoWSOVLhvvvmGWVhYMDMzM3bs2DHGGGMpKSlswIAB3GNpnmh0LfPy8OFDZmFhwUxMTJivry/39hkTtz2ad8UXL14sLY8dO5ZbjH79+rHc3FyWk5NT5g9PFVEFTe7vmgidOnViBw4cKPcPT5qfz4cfflju+qoQgzHGlEolY6wkEe3SpYtsccozcOBA2dounTBs376dW9u6jvu8zwfGxsZsxowZUoVW0ZVM1dTfkdfl4+PDBg4cyKZOncpOnDjBBg0axKXdlyXHd/vevXtsxowZzNLSku3cuZNr2+okXqVSsRYtWkjree0XxkqqP3fq1IlNnDiReXl5sYkTJ7LOnTtzf0K/adMm1rJlS9asWTO2ZcsWNnHiRDZq1Ch2+PBhrnFI5UNj5kiFmzVrFlxdXaGnpycVPWnQoAH27NnDPRbT6P+ua5kHEWNLRLp165a0HBsbKy3/8ccf3GJcuXIFQ4cOlfaFZoVGnpW4EhMTy4y7UccRNUksT6NHj9Y5SJ/n9mRnZ+PChQtlfld4T7Ks2X52dna566tCDEDMGBZ1ZURNjDFZxsmIqNAqagygyPHTRUVF+Pvvv1G/fv0yr/Ga01BUwaUX4fk7JGIssIgxurNmzUJERATatGkjrUtOTsb06dNx9OhRbnHUY7Ozs7PRsmVLnDp1Cl26dOHWPqm8KJkjFY4xhp9++gmRkZFIT0+HqakpRo8ejSlTpnCPlZOTgxs3bqC4uBi5ublayzxVq1YNp06dkk6o/v7+SE5OhqenJ/bu3cstTmpqKoKCgsAY01pOS0vjFgMQUzmsS5cuQsont2zZEv7+/rLHEbVvVq9ezbU9Xd577z2uU1HoUlxcjLy8PBQXF5dZrkoxAOCnn35CWFgYrKysMHHiRAAl0yIsXryYWwxd32VeU1JoEpEwZGVllbv+4cOHXOPUrFkT48ePx/jx4/HPP/8gOjoarq6uyMjIwLlz57jF+f777/HVV1+hYcOGePjwIdatW6d18+Orr77iEkdkwaXybN26FRMmTOBWOMbOzg5paWnw9PRE165doVAoZJnS4csvv5Sq2aqri96+fZtrElRYWKiVyAElv5/qCsG81KhRA4aGhjA0NETXrl0pkfsPUTDetyIJeUm+vr5ISkrC4sWLYWpqivT0dKxcuRKtW7fGvHnzuMZ6XsWtF1Ude113795FeHg45s+fz63NzZs363zNycmJWxwTExO0bdsWjDEkJSVpLeu6+HpZI0eOxK5du7i09TzW1tbSPHNyErVvynsyo8bzomfFihVckxBdrK2tpaeyALSWee230jE0yf3duHHjBgICAhAUFCRrHLmoE4Zjx47hypUrWLdunZCEYdCgQThy5Ai39u7evYsWLVoAKKmaq+4VEhsbC0tLS25xzM3Ncfr0aRgZGeHx48da81zKKS0tDZGRkYiKihIylyrv/ePs7FzuTUTeUzpo7ntNDx8+RIMGDbjEsLe3x1dffYWOHTtK6xITE7FgwQJER0dziQGUJIi1a9cGYwyZmZnSskKhqLBpmIgYlMyRCqdUKnH8+HHo6elJ6woLCzFgwABhE3qrnwjypk7gDhw4gLZt22LAgAFwdHTkHkdTUVERdu3aJXsJb95OnTql8wlgv379uMW5cOECunfvzq29lyHHvtHT04O5ubmUuGkmQTwvepYtW6Zz/4h4YifCs2fPYGRkxK29zZs3Y8WKFSgsLMSKFSvw888/Iy8vD1OmTMGgQYO4xBDVzbY8IhMGGxsbrpOT9+/fX+oJoGuZBysrK8TExEi/l0OGDJF+Li+JeFVRUVEYMWIEAODcuXPo3bs3gJLvIM+bR7rwTuZE0dzf9vb2UnLF83uQmpoKJycn1K5dG40bN0ZqaiqysrKwceNGNGnShEsM8t9G3SxJhdPX19dK5NTrqlevzj1WVlYWfvrpJzDGMHHiRKSmpmLZsmXIyMjgejEiYmwJAFy6dAk+Pj4ASi6oz58/j+DgYNja2nJP5nJzc1FUVISaNWsCAGJiYhAQEIADBw5waT8mJkZaVj81OXDgAJKTk7mOX5g5c2aZJz8FBQX466+/cPfuXW5xRO2buLg47Nq1C9evX0fXrl0xatQoWcZlWlhYSMsKhQKpqalYu3YtiouLuSZzS5YsgbW1tdYk4SdPnsSJEyewbNkyLjGKiopw5MgRMMYwePBgPHnyBP7+/jh8+LDWHFCvS8QYFlHdbIHynwLb2Nhg4MCBssaQYwygqPHTenp6Wt0q1T/zHgscGBgoJXOLFi2S2uadzHl6epa5ecAY0xpXzYOomxQixs82btwYx44dQ0pKCjIyMmBqaorGjRtza1/T8ePHsX37dukG9fjx46FUKmWJRSoPSuZIhUtOTi4zaaocJweg5AShVCrx9OlTKJVKtGvXDgsXLkSnTp24xhE1GH3GjBn4+uuv8eTJE/Tv3x+LFy/GL7/8An19vr/aPj4+CAsLQ35+PpydnXHu3Dm0b98ea9as4RoDKLnQ3r59O4KCgmBjY4ODBw9yiwEA58+fl5b/+ecfBAcHY+fOnXB3d+caR9S+MTc3lwpGxMXFwdnZGebm5ggODuYaZ/DgwQCApKQk+Pv74969e1i5ciU+/PBDrnGOHj2K5cuXa61TKpVYtGgRt2TO0dERdevWRWZmJgICAqBQKDBt2jTpO8iLiDEs9+/f1/rZ2NgYrVu3lqXbY8eOHWV/CixqDKCo8dMiulSKNGzYsJda/6pE3aQQMX52/fr1OhPT6dOnc4sTEBCAQ4cOYc6cOWjSpAlSU1OxevVqJCYmwsPDg1scUvlQMkcqXFhYWLnreZ8cgJLKVV5eXgBKLky2bNlS5qkgD6IGoxsZGUnV0Fq0aIGZM2dya1tTdHQ0rl+/jmfPnqFJkya4dOkS9zuLz549ww8//ICwsDAMHz4cBw8elG0czqNHj7B27VocP34crq6uOHv2LAwMDLjGELVv8vLycOjQIURHRyMvLw/Tp0/nVoRAU0JCAvz8/FBYWAhPT0+pGxdvhoaG5a7n+aT+8ePHiIiIAAC0atUK169f577/gZLuVebm5tIYFvUyzzEshw4d0vr52bNnuHz5MpycnODs7MwlhpqIp8Byj11W69ChA/z8/ACUjC3VXOap9I1KTeqYPIgquGRlZaX1M2MMJ06cQERERJnXXkdOTg63tp5HT09PqqJcepmXt956i1tbz7Nz506cPHlSumFoZmaGfv36QalUUjL3hqMxc6RSKCoqQlxcnNQ1wMLCQpanWWZmZpg1axYYY/j2228xa9Ys6TWed8hKk2tsSYMGDaBUKsEYw6lTp6Rl3mX2Ncd92Nra4vDhw9JdeV7jPurXr4+GDRvCyckJRkZGWncyee6b6dOn49dff4WHhwccHBy0Tto8x7CI2jdvvfUWOnToAHt7e9SuXVu2z01PTw/t27dHu3btAGhXOOW5PZ9++ik6deqkVc02JCQEiYmJWLduHZcY77//Pnx9fcEYw/z587Fq1SrpNZ7TLFSUoqIiKJVKnDlzRrYYcXFxmD59OvenwBU5BhDgP366Y8eOqFGjBkaPHo0ePXpovcYz+RFVcAkoeZp18uRJRERE4Pjx45g0aRL69+/PbZoFQNxYYBE++eQThIaGyh5HqVTi5MmT/3o9eXNQMkcqXGJiIsaNG4cuXbpIg4OvXLmCbdu2cb9LquuEp1AoMGnSJG5xdFUYZIyhQ4cO3OKU7mKlplAo0LRpU25xdFX/4znuQ9TFiOZYLPVFoxzz2YnaN5s2bdJ58cvzc9O1PQDQrFkzbnFUKpVULIQxhmrVqsHGxgYLFy7U+dTuZenqrqlQKLgXc6moMSx9+/blnsyVfgpsY2MDBwcH1KtXj1sMUd8zUeOnAeDmzZuIiIhAQkICWrdujUmTJqFz585cY5RHjoJLU6dOxe3bt2FjY4OPP/4Y06dPL/N0mIf4+HghY4FFjNHlXVRHl8WLF6OwsBBeXl4wMTFBdnY2Vq1aBYVCgS+//FL2+KTiUDJHKtzAgQOxfv162SfU1OXOnTuIjIzkOg1CRd1VfPLkCXbv3o3IyEjuY83Kw/Mutq5S4VeuXKmSE62XJmrf3Lp1C8XFxWXmNXpduqrlbdmyheuNkIqSk5OD/fv34+OPP+bWZnljWNasWQNbW1tu3Z5KT3GgUqlw5swZdOnShXtiKuIp8OnTp7V+lmsM4ODBg6Xx00ePHkW7du0wb9487uOn1bKzs7F//34EBgbCzs4OCxcu5Nr+8wourVy5kluc8ePH4+nTpxg2bBhGjRoFJycn2c81cj0FBoDevXuXO6+gpaUlYmNjucRQ99LQJEcvDcYYQkNDsWPHDvzzzz+oWbMmxo4di8mTJwuZK5ZUHBozRyqcqAk1NZU3ZQBPoioMAiVJQlRUFA4cOICLFy9i5cqVsnbpyMjIwK5du7B//37o6elxO5G7urpKdy+HDRsmLc+aNYvrXU03NzeEhIQAKLnoURfbGDduHLZt28YtDiBm39jb2yMsLAy1atXCypUrcerUKdSpUwetWrXiehGnq1repk2buCZzz7vw4H0jRJ3A7d69G7m5udzHAYoYw1KjRg2tn+vVq4cVK1Zw7QGgFhgYKPtFoagxgKLGT2/evBkxMTGoXr06hg0bhiNHjpTZZzyIKrj0008/QaVSSTcpbt68iW+//RbW1tZcz3GixgKLGKPbsmVLnYV9eFq0aBFWrlwJV1dX2WORyoWSOVLhTExMkJiYWGZCTRMTE+6xRE0ZIKrCoK2tLf73v/9hzJgx2Lp1K0aOHInx48dzjQGUJHARERE4fPgw3nnnHSQmJiI2NpZr0QhRpcI1q6Rq3nnNyMjgGkfUvnn69Clq1aqF4uJibNy4EUlJSdDX1+c6N59I8+fP1/pZXXiF53xMO3bswO7du6FSqfDRRx/h8ePHsvQC0NfXL3MxXd661+Hk5CTd5QeAlJQUPH36lFv7mkonU3I8BS6voqh6DCDPZE6zSEh+fr7WsZnnWNPJkyejffv2MDY2xo0bN7B69WpZJnIWVXAJKEmAPvroI3z00UdQqVSIiYmBv7+/zmJmr6J+/fpaT4Hz8/Oxc+dOAHz3T6dOnfD999+XGaPL8wmtkZER1y7CumhWaib/LZTMkQoXFBRU7oSaclQ1EzVlgKi7il27dkVCQgIuXboEMzMz2e6aN2nSBO7u7oiMjISxsTGGDBnCvfqfqFLhuj4j3p+dqH1TUFCAoqIinD9/Hl27dpUSBZ6ltQFx1fLUicHPP/+Mr7/+GvXr18fWrVu53vX39PTE0KFD8dlnn6Fdu3ayFdbo27cvvLy8yoxhsbS05BZj7dq12LhxIwwMDDB06FAcO3YMxsbGaNOmDQIDA7nFAcQ9BS5NjuO0ZjdHzWXev6e8fw91SUxMxJgxY8AYw927d6Vl3l35yuuJUb16dYwdO5ZbDEDMU2CgZCqMFStWoGfPnlpjdHk+SSuvaundu3cRERHBdXjH7du3dVZP5Vk5lVQ+NGaOVBopKSn4448/YGpqikaNGskWRz1lwLFjx3DlyhWsW7eO+5QBoioMql2+fBkRERE4ePAglEolrK2tYW9vz6395ORkRERE4MyZM2jTpg3i4+O53wWcPHmyztd4JvYmJiZo27YtGGNISkqSlpOTk5GZmcktjprc+2bHjh3w9fWFSqXCTz/9hK5du+L27dv44osvsGfPHm5xRBWoiYiIQGBgILp06YI5c+ZwLRaj6ezZs4iIiEBSUpLU7Zp3UQoRY1h69uyJuLg4qFQqtGvXDnfu3IGenh769OmDs2fPcomh1q9fP5w+fenah70AACAASURBVBrFxcVo3bq11lPg0mPdXpXIMYClyTF+OigoSDrma4479fPze+60BS9LVMElkcWDNMk1Flik0sM7+vfvzzUJ7tq1K7799ttye7PwrJxKKh9K5kilUFEV3+SaMkBUhcHy/Pbbb4iIiJCtetWtW7cQERGBo0ePonbt2oiKipIlzptI7n2jiXeJdVH09PTQvHlz1K1bt0y1UZ7d0jSdO3cOEREROHHiBC5fvsyt3YULF8r6xArQrpSna5mXXr16ITY2FufPn8fatWsRGRkJgG+xiNI3DYyMjNCxY0dZxgAC5Y+fdnR05Na+yP2jSa6CSzk5OcjPz0edOnW0YhkaGnKd2kXUU2ARY3RLD+/w8fHB4cOHubStydra+o2bpJ78O9TNklS48iq+rV69GomJidwnunzw4EGZdQ4ODty7QIoYW6J2+PBhREREICMjA6amphg1apSsyULr1q2xYMECLFiwALdv3+bW7vPKZ8vdTUiN5xxj165dQ7Vq1dC2bVusWrUKf//9NwBwrZYIiCux3qBBAzRt2lSrSIQcSZaIbmlDhw7FgQMHpJ979+4tyyToIsawXLhwQZqMPCkpSVpOTk7mHuuzzz5D9+7dpafAQEnXrrfffptbDFFjAEWNnxZJRMElFxcXLFiwQCuZS0lJgY+PD7Zv384tjqixwCLG6Ioa3jF79uwy63Jzc7Fv3z7u5x1SuVAyRyqciIpvaprdZ9QXC4mJibh27RqKioq4xRF1VzE0NFTqd29qaor09HT4+/sjLS0NLi4u3OIEBwejY8eOsLS0hIWFBYqLi1FUVISJEydqTbz+OnJycpCdnQ17e3vY2dnJUvENAIYPH44PPvgAAwcOhIGBgda0ETyTOU9PT2zduhUAsHfvXvj6+qKgoADe3t7Yt28ftzijR4+WSqwrlUq0a9cOCxcu5F5i/YsvvsC5c+fQuHFjjBgxAlZWVrJdlGhKTExEeHg4Dh06hISEBC5t5uXlcWnnRUSMYcnKynru6zyn9nB0dCzz1KpVq1Zluka+DlFjAEVdYKu/A4wxreU7d+5wjSOyGFbp7sjvv/8+0tPTucYRNRZYxBjds2fPSsM7goKCcO3aNezdu5f78I4PP/wQQMm5dN++fdizZ48sVXpJ5UPJHKlwIiq+qWneOYyNjYW/vz/MzMzwww8/cI0j6q5iWFgYDh06BGNjYwAlJyYLCwsMGTKEazK3bds2aUyMsbExTpw4AcYYBg8ezC2ZO3DgADIzMxEdHY0VK1ZAoVDAxcUFAwcO5NK+WmpqKqKionD06FGYmJjAwcEBtra23Au65Obmom7dugCAjz76SNr3vr6+XOOIKrGuTkpSUlKwa9curFy5EsOHD8enn37KPdbVq1cRHh6OhIQE3L59G5s3b+Y6Hkf9NEuTHE8Z69Wrh2HDhnGvyPoyeE7tIeIp8Pbt2/Hbb7+VOwaQJ1EX2JoVHocNG1buMg+iCi6V125xcTH377iIp8CA9hjdkJAQWcboZmdno3Hjxpg1axZmzZolDe9Ys2YN194Toqr0ksqHkjlS4URUfNO0f/9+fPPNN2jZsiVWr16N1q1bc48h6q6ivr6+lMipGRsbc0+ENdtbu3YtgJKTOs+nmQBQq1Yt9OzZE6mpqTh58iTu3r3LtX0AeOeddzBt2jRMmzYNly9fhouLC3bv3s29eipjTEoQ1E+Ei4qK8OzZM65xRJVYB4BHjx7h0KFDOHfuHJo2bYpu3bpxbR8A2rZti969e8PNzQ0rVqzAkCFDuF/If/DBB0LGltSuXbvCp4jgeZEt4ilwjRo1oFAoYGRkhObNm0s3JnTNB/Y6RFxgv6jwBK+xc+opHdQFlzIyMvD5559zL7g0duxYTJo0SeoNkpaWBj8/P4wbN45bDEDMU2CgpNt78+bNkZubK3WL5n1jZ/jw4Vr7uFGjRvjss8/w2WefcWlfTVSVXlL5UDJHKtzy5csRGhqKESNGIDs7GyYmJnB0dOT6ZEmtY8eO0NPTw2effYZ3330Xt27dkuYd49nFTtRdxQcPHpQ5uTHGkJKSwjWOsbExUlJS0KRJE6mLzf3797leYC1btgyXLl1CmzZtMHLkSOlpE2/379/Hrl27EBsbi4YNG8Lf31+WYjsTJkzApEmTMH/+fOmix9fXFxMmTOAaR1eJdd6sra2hUqng4OCABQsWSE8yr1+/jvbt23OL8/nnn+PAgQMICAhAeno69xsGIlWGMSw8n9CIeAosagygqPHTL8L7iVaXLl3QpUsXrFy5Uiq4xDOZc3d3R6tWrbBmzRqp+rSTkxMGDBjALQYgbiywqKkjREhJScHZs2fx3XffSVV6f/vtN+5VeknlQ9UsyX9KRZVVVuNdYVBUufjr16/D1dUVffr0gampKVJTUxEXF4fQ0FC0bduWS4wWLVqgXr160s8KhUKWrm/VqlVD9+7dYWlpCT09Pa2LXd5z8Rw7dgzbtm1DRkYGGjVqhLFjx6J///5cY6ilpaVJ3y85pvYoPXWE5v7hVfVN019//YWoqCjs3bsXxsbG6Nu3L7cunampqWjcuDGXtv6N8sawlC68IBeeFe7MzMww6/+1d+dxUZXt/8A/M7JrgithGpYlQrKohbKkgCLiVqZAYqSmGKJmhmC54poCGqmRZaalWTKDhRu5sIqIkompGIpbDPK4ICqICAPn9we/OV8Q0Mz7nDMM1/v1el7P4SDnvoBpONe57+u6P/4YHMfhyy+/rLPEWogtVxrCqgawdkt4Ieunn4RlV8uGmmF5eXkxufajVCoVrl27hhdeeEGQ9xsvLy9+FvjQoUOwtrbGnDlzmNcCP0qIGl3Ndji1Cd2hFxCuSy/RPpTMEcmJ0VzjURzH4datW2jfvr0gtQViPVUUU2VlJTIzM/kbhb59+0JfX5/Z9dPS0kRZkva4n39T3IunoKAA/v7+4DgOnTt3hkqlglwux7Zt25gmLOvWrcOMGTOYXe9pFBUVYfv27czGd3JyqpOMAsCtW7dw8eJFpjfyj9awbNu2jXkNS0FBATp06AADAwOUl5dj69atqK6uRkBAAExMTHDv3j20bt2ayVhiPTx6HCFa+mvqp/X19REWFlavnlJIrJLtxpphjRkzhukqF5VKhXHjxoHjOHTp0gX5+fmCvN/U/j337NkTf/31lyC1wEDDNbp9+/ZlVq4g1pYBU6dOha+vL9zc3ETZcJ1oD0rmiORqbziredPTNNc4cOAA07Hu3r2L6dOnIzMzEx07dsTNmzfh5OSEtWvXwtTUlNk4Yj1V1NyU1lZZWYmioiKm9WaP2xCYVQIm9L5LGo+2pReKWL+bESNGICwsDG+++SZ/Lj09HatWrWLaNVOs309VVRUOHDjAvwcUFxcjMjIS+/fvF+Tp8pUrVxAVFYVTp05hxowZj90i42l16dKlTg3L0KFDme75BdRsq5CcnAxDQ0P4+/uje/fu6NixIw4dOiTYHpBCzwI/Dssb49r106GhoYLUT9dWXl7OL+vT7MnG6iGWm5tbnWZYQE3XVm9vb6SkpDzz9TXEer8Raxa4do2upnlYQkICs+sD4iVzSUlJUCqVSE9Ph4uLC1/jKlQSTLQH1cwRyYnVXAOoeXI1dOjQOh3Gtm/fjqCgIKZ75IjVYbD2PlalpaXYsGEDduzYgaCgIKbj1P7jppnR2Lt3L86fP8+soUdpaSlycnIa/BzLmiyx2tKL9bspLS2tc2MF1GzivGDBAqbjaBqtNITlzdW7776Ltm3b4u7du1i3bh1kMhmmTp3KN3hg5fTp01i1ahVu376NkJAQpm3vNcSoYTEwMIChoSHKysqQlZWF7du3AwC/oTdLYs0CPw6rGQex6qfDw8MxcOBAvPnmm+jduzeef/55qNVquLq6YsWKFcwehonVDEus9xuxaoHFqNF99KG0UBu6e3h4wMPDAxzH4fDhw1AqlQgJCUG/fv3w9ddfMxuHaB9K5ojkxGiuoXHt2rV6DSj8/f3x7bffMh1H7A6D0dHRSEpKwuTJk3HkyBHmbfY1N9JVVVX4+eefERMTA09PT6Z/iPLz8xEVFVWvIQDrmqzaDRY0N4ZC1S+I8bvR19dHQUFBnRmSa9euMV0CC9TUGrZq1UrwNvu3bt2CQqEAUNO9Licnh/nPbOjQoSgoKEBoaCh69eoFmUzGP0hg+eAAAFxcXPhunBkZGdiyZQvTGhaO43Du3DkkJyfD09OTP3///n0m168tKCgIy5YtqzcrM3XqVKazMo/D6vXn4+MDoOa9WqVS8edZ7zeZlJSE8PBwADWddDWz26xr2cRqhiXW+41m2a7Qs8AffvghPvzwQ75G18DAAD4+PkxrdPX19UXZ0F1DJpPBzMwMZmZm0NfXf+JelKTpo2SOSC4qKgp+fn4NNtcQwqM1JEK80Yn1VDE4OBgnTpzA9OnT8dlnn0Eul0OtVkOtVvPLeFgoLy/Hd999h61bt+Ktt97Cvn37mO7FBNQsdxGikcajxGpLL9bvZs2aNRg9ejRcXFz42ZKjR49i48aNzMYAgOeffx7vv/8+02s2pKioCAkJCeA4DiYmJnVqzFjdZHfs2BHm5uZITExEYmIif571g4NHa1icnZ2Zb+C7fv16LFq0CC1btkRkZCSAms65mg2EWRJjVuZJNYDx8fFMxlm0aBEA4euna8+M/fLLL/xxZWUl03Ea+zszd+5cpuOsXr1alPcbsWeB27Vrh8DAQAQGBvI1uqyItaH7iRMnoFAocODAAdjY2MDHxwfz5s0T5ME40S5UM0e0wuOaa7DqXgYAKSkp+PTTTzFu3Dj+D8Qvv/yCzz//XJDmG0I/VXR3d+ePH51lYlnf1KFDB1hYWGD8+PEwMjKqc9PDaqZx2rRp+Oqrr5hc63HEql8Q63cDAGq1GkePHuVbhbMs3tfYuHEjAgMD651n3aFV6o6zLElVw1JVVYW4uDim9X8AMHjwYGzevLnerMyECROY1TeLVQMoVv20j48PFi5cWKdeOjs7G0uXLkVcXByzccSkeb/RdOkV4v1GrNo8MWp0P/vsM2RlZaFPnz7w8/PD/PnzmdfOAsC4cePg5+cHLy8vSuCaGUrmiNZj3XihuLgYe/bs4RPHYcOGoU2bNsyuD0hfW8L6BnvLli2NPrVm2cXu3Llz4DgONjY2qKiowKZNm/D9998zaxENgF/SW1txcTE2btyIsLAwZuOI6cKFC9ixYwf/mvbz88Mrr7zCdIy7d+9i+/btgndonTdvHpYvX87seg0ZOXIkdu3aBQCYMmUKv8xaqCYvtWtYDh8+zLSG5eTJk/wy6IULF+Lo0aPYsGEDhgwZwvzneObMGX6LkkdnZV577TUmY7i5uSElJQVlZWWwt7fn69hY/278/f0xdOjQOsvut2/fjt27dzOtny4oKMCECRNgZmbGrzwpKSmplxQ/K7EaLv3555+wsrJCy5YtcfPmTXzxxRfgOI6vPWSlsYdurB/G+fj48DW6d+/e5Wt0hw0bxvyhi2ZD93379sHNzY35hu5fffUVpk2bBgDYuXMn3nnnHQA1W+401b9t5N+hZZZE67F83qCpi+nTpw9/rrCwEIWFhUxrZaSoLSksLERcXBz27NkDuVzO9MnfhAkTmF2rMdOnT8eFCxdQWloKa2trXLlyBW+//TbzVu45OTkYPXo0AGDVqlU4dOgQ0tLSmC8hDA0NbTQBZrmfnVKpxKpVqzB16lQ4OjryrcPDwsL475MFzXLBO3fuwM3NDdbW1pg3bx7zDq21G8cIpaSkhD/WJAsA+w2cNYSsYZk2bRpWr16N4uJieHh4YMGCBTh27BjzmRKgpmlIeno6Pwvs6OiIVatWMR1LrBpAseqnX3jhBRw8eBD5+fn8w5bOnTszX2YpVsOl4OBgZGRkAKh5kOfn54cOHTpgwoQJ+P3335mNI1Ztnhg1uhpCb+geFxfHJ3Pr16/nk7nff/+dkjkdR8kc0Xos6xh69uwJR0dHPnHT3LyxrpURq+NXYWEhFAoF9u/fD3Nzc5w5cwbp6enM/xi98cYb9X4PrJuGnDp1CocPH0ZVVRW6du2Ks2fPMtsfq7aFCxciPj4excXFcHJywrZt2wSZCRo+fDjzazbkiy++QHJyMlq1asWf8/X1xZAhQ5gmc2J1aM3Ly2v0xoNVElz7tdzYMQti1LAYGRnByckJAPDyyy8Lvhfg5cuXkZqayicm5ubmTGeBxawBFKN+WqNLly54/vnnsX//fsydOxe5ubk4duwY0zHEaLhkZGQEuVyOu3fv4sqVK/zKjKioKKbjiFULLEaN7uLFi+u8txgbG8PW1hbLli1jcn1CKJkjkispKcFzzz3X6OdZPi3PzMxEXFwccnJy0KtXL4wZM4ZZPV5tYj1V7NKlC4KCgqBUKmFsbAxvb29BnioKcVPwqIcPH/LLLC0sLFBQUMB3mWM5a9qyZUtYWFjAwsIC9vb2gtwkAjXNDsTYwNXAwKBOIgcArVq1Yv5aE6tDa7t27TB8+HBBu2aqVCq+o+ndu3f5Y9Y382vWrIGfnx8WL14sWA3LmTNn4OvrC47jcOnSJf5YJpMhNjaW6VhizALb2trWi/ull15C9+7dmVxfIzw8HIMHD26wfpqliooK/P7774iLi0NxcTEuX76MhIQE5svtxWq4pK+vj4SEBKSnp/PvnRzHMZ85FWMWGABGjx7NP5AcPXo0v6SfZVfTfv361fm4vLwciYmJ2LNnD9MtUTQPwjiOq3N88eJFZmMQ7UQ1c0RyT6qFePTpKSuZmZkIDg6Go6NjnRtTFsSoLQGA8+fPQ6FQ4PDhw7CyssLx48cFWaZmZ2eHXr16wcfHB15eXswTBQCYOHFig+dZz5q2bt0aPXr0AMdxyM3N5Y9Zb02gaX5x5MgRODs7C9b8Ijg4GPb29vjwww/5cxs3bsSff/7JdG+hH374ocHzMpmM6RJVsRrUiEGMGparV682eF4mk+HFF19kMoaGi4sL9u/fX+fhQWlpKYYMGYL09HQmY4hZAyhG/bSZmRnGjRuHefPmoVOnToJsSg2I13BJpVJh7dq1MDExQVhYGExMTHDp0iUkJydj0qRJzMYBxKkFFqNGtzGs60AfV7s8YMAAZuMQ7UPJHJGcUE0HGvLgwQMkJCRg165dePDgATw9PTFq1Ci0a9eO+VhidBis7cKFC1AoFDh06BCysrLq1AWxcPz4cSiVShw8eBC2trZ8Yif0jF1mZma9J5tNiZDNL4CaGc2lS5fiwIEDfKI4ePBgzJ8/X/DfzcWLF6FUKjFnzhxm19yzZ4/gS1Qb2/wcYDvLWPu9rbFj1oTakBgQpymFs7MzXwM4YcIELFiwAFOnTmX+3qmpn24Iy5UA+/fvh1KpREFBAQYNGoTdu3eL+rCCdTOsJ2H12q49C6x5IPrNN98wrwUW8/6jtvLycgwcOBBHjhwRfKz3338fP/74o+DjEOnQMksiOc0mzrUJtYlzhw4d8Nprr2HkyJEwMzNDRUUFduzYAYD9Zt5C15Y86tVXX8XcuXMxd+5cDBo0iPn1HR0d+d/TiRMnMGfOHAQEBODOnTvMxzp27BgUCgX+/PNPdO/eXdBk7syZM4iNjUVCQgLTrpkaQm/gamhoiGXLltWrv9i2bVu9Bg8sXLp0CbGxsdi7dy969OiBgQMHMr3+6tWrsWbNmjr1rBqsbrrmz5+P7t27Y9SoUbCwsBB8I3ShibUhsbW1Nb755pt6s8A9evRgNoZYNYBi1U97eXnBy8sLVVVVSEpKQm5uLjw9PWFra4s1a9YwG6c2IZthPQmr/5bEqgUWo0bXx8enzvtYeXk5VCoVli5dyuT6T1JYWCjKOEQ6lMwRyYm1iTNQU1wvZP2ShlgdBhsj1D5WGRkZUCqVSElJQZ8+fZh2yMrMzIRCocBff/0FFxcXZGVlMW15X9vp06cRGxuLrKws5OXl4YcffmC+h5nUG7j++OOPTJO5lStXIjExEd26dcO7776L5ORk5s0IANTpiFddXY24uDisXbsWr7/+OrMxrl+/jkOHDuHXX3/FsWPH4ObmhjFjxjCfwRCjhkWsDYmBmhvspUuXwtHRsc4s8JdffslsDLFqAMWqn9Zo0aIFPD094enpiaqqKqabUgPiNcN6ElZ/X8WqBRajRvfR5jBGRkYwNzfHzZs3BRuzNjHueYi0KJkjzYoYLfYB8Z4qPvrED6h5Mnr69GlmYwA12wakpaXByckJY8aMQWRkJFq0aMF0DA8PD3zwwQdQKpUwNTVlPiur0aNHDzg7O2PKlClYunQpvL294eLiwnwcMZpfiGn37t2wtLTEW2+9BVdXV+a/fw1DQ0NUVlZiy5Yt+O677zB48GDs3bsXHTp0YDaGvr4+vL294e3tjbS0NMyaNQuXLl1CdHQ0szEAYOvWrfxx7aWjLJeR9urVC1lZWTh58iS6d+8u6I2bGLPAjc2Os/6+aq80yMzMxIQJEwSpn25IixYtsHXrVgQEBDC7pljNsJ6EVVIkxiwwUFPT2L9/f6bXfJSlpWWD58eNG4cDBw4wG6exrtOsSy6I9qFkjkhu3bp1jdYvsKxdAICOHTvixRdfrDNzJcSSTrGeKrJuB92Yt99+G2vXrhVsxg+omS3ZtWsXAgMDwXEcrl27huLiYuYNCWbNmoW9e/di3bp1uHbtGqqqqpheX8Pf3x8ymQyJiYn1PseqSxrQ8H52HMfV2T+NhSNHjkClUkGpVCImJgZnz55FfHw8BgwYADMzM2bjREZGIi4uDn5+fkhKSkLLli2ZXVsjNTUVO3fuxD///AMnJyfExsaiW7duzMdprOnA+++/z6whgaZZiGZD4sLCQsyaNYv5hsSPw3IW+NEbX6FqAB+tnw4ODsaoUaOYXV9sOTk5UCgUGDVqFKysrARZ/g4AkyZNeuwS3sWLFzMZR4xZYAAICQlher2nwXo2cMiQIfzSzdrNl2bNmsV0HKJ9qAEKkZxcLheldgGoWQOfkZGBzp0745133sGAAQMEmWEQq8OgWBqbARSi/TlQ0yFv165diIuLQ2FhIb9JLUtFRUXYuXMn4uPjYWxsjDfffBMfffQRs+s3dlMTFRXF9EnpkiVL+MTg0c6vQnYwKygogFKpxM6dO5kuh33ppZdgZmYGPT29el35WD1wkcvleP311/nEofZrW4jX86M8PT1x8OBBwa5/6tQpKJVK0WpyBg8ezHSGoaEaQA8PD1hYWDAbo1WrVnXqp2u/BljWTzf2sEWpVOLy5cvMxqlNyGZYUjUM0WBdC+zu7g6ZTCZojW5DD6s5jsOUKVOYNkCRouES0Q40M0ckJ2btgqbGKz8/H3FxcVi+fDneeustpjfxgHhPFcUixgxgZWUllEolVCoVrKys4O/vD39/f5SWljIfq6qqCtXV1QgMDERgYCCKiorw66+/Mh1j0aJFDZ5n1cJdIyUlha/3E/KP9qVLl7By5UoUFBTAysoKn332GWbOnImZM2cyHee9994T/OmyUDfR/xbLJYMLFy7EkiVLANR06dQ8SGpsK4lnIcYssFg1gGLVTz/33HMNPmwRsmOrkM2wNDWNtQn5YO9RrGuBxajRjYyMbPA8670TSfNFyRyRnNi1Czdv3kRCQgIyMjLw4osvonfv3szHELvDoNAaW/PP8om8r68vunTpgp49e0KhUCApKQnR0dH1lqs+q40bN2LFihWwsLDAjRs3sHbtWgwdOhSTJ09mOk5jmmox+vjx4zFr1iz07NkTaWlpmDp1KpRKJfNxaj+pjouL45O5U6dOMRvjxIkT/J5vGRkZcHZ2BlBzo8hyzzwxalhqPxxQKpX8rFJ2djazMTTESEzEqgEUq35arIctjWG9NL5bt26NJidNkRg1ups3b0ZBQQGuXbuGl19+WZCtkIC6zYNqH589e1aQ8Yj2oGSOSE7M2gV3d3c8fPgQo0aNwty5c/kC8ZycHOb1eQ1h/VRRaixXaWtqYwBgypQpcHNzY3bt2jZu3Ihz587ByMgIt27dgo+PD9MaNg2xmtM09gec9ZNyfX19PgHq3r078258Ylq/fj3/vcyfP5+/wd6yZQvTZE7XaljESEzEqgEUq35aLGK93xgZGTX6cI8lsWqBxajRXbx4MXbt2gUrKyucPn0an3zyCSZOnMh8HCG21iFNAyVzRHJi7v3WtWtXADXJW+117ELU5+mSsrKyeuc4jkN1dTWzMa5du8Zv5qxpgKL5mOXrwNjYGBzHoaysDCYmJqiursaDBw/AcRxMTEyYjSNWcxqx/oDXXl5VO3EE2NaZ6dLTZTFmGXXp51Wbg4MDHBwcsHz5cr4GkGUyN3v2bFHqp8V62CLW+41Ys3JiLU+NiYmBmZkZtm/fjp9//hkA+6T+999/xx9//AGZTIYHDx7A29tbkGROjCSbaCdK5ojkatcuaP6/srISt27dYj7W5s2bmV+zIWI9VRTLsGHD6hSJC2HevHn1Plar1cxfB3K5HMOGDeO/F7lcjqFDh0ImkzGdaRDrD6tY44jVMl6M5FSlUiEmJgYcx9U5LigoEHxs1uzt7TF37lzmXV8bIkZiIlYNoFj102I9bBHrfWDGjBn1GoZUVlaiqKgIly5dYjaOWMtTxajRraqqwt9//83/zMrKyviHyWKsCCK6j5I5IjlN7cLFixehUCiwd+9e9OjRAwMHDmQ+VkVFBZYuXQqlUonKykoYGBjwmzmz3JNHiqJ3IYmxqfv48eMBCP86aOx7uXbtGtNxdI1YLePFuCmt/eCg9vHcuXOZjiPGrNnGjRsRFhaGtm3bYsWKFWjbti2zaz9KjMREzBpAMeqndW225OjRo/xxaWkpNmzYgB07diAoKEjCqP47MWbPX3vtNURERNT5ODIyklYEEWYomSOSW7lyJRITE9GtWze8++67SElJwcaNGwUZ6+OPP0anTp2QnZ0NQ0NDPHz4EGvWrMHMmTOZbhkgddE7a5on5Y+SyWRYsGAB46hoeAAAHX5JREFUkzEefR0kJycL9jrQKCwsRFxcHPbs2QO5XM40KdFFDbWMf9yeU9pK8+BA48KFC6iuroaVlRXTccRIfrp27YrY2FiEhITA2toalpaWgtV+6VJiInX9dFN28+ZNREdHIykpCZMnT8aRI0eYb1Au1vJUMTS2Iujhw4ciR0J0FSVzRHK7d++GpaUl3nrrLbi6ugq6MfXZs2f5OiygppPVZ599JlizDV3Rt29f/lgmk0GlUiE6OhrV1dXMkrlHXwdC1K8ANQmcQqHA/v37YW5ujjNnziA9PZ35zYiuEatlvBhGjhyJrVu3wtTUFMuXL0dqairatGmDV155BcuXL2c2jhjJz99//42QkBB0794d58+fh6mpqeBjCkmsGkCqn/5vgoODceLECUyfPh2fffYZ5HI51Go11Go105pjsWuBhXy9eXp6IioqCvb29vw5hUKBqKgoHDt2jNk4pPmiZI5I7siRI1CpVFAqlYiJicHZs2cRHx+PAQMGwMzMjOlYHMdBrVZDT+//XvoVFRXMa8F06akiAHh5eQEAcnNzERkZicuXL2P58uUYMWIEszHEeh106dIFQUFBUCqVMDY2hre3NyVy/4JYLePFcOfOHZiamqK6uhrff/89cnNzoaenh/79+0sd2lObNWsWVq9erTOzSWLVAIpVP61rzp07BxMTE3z//ff8z1Dzt60p1hyLkTR+8803CAsLQ7t27fDuu+9ixYoV6NWrFw4ePCj42KR5kHFCdjQg5D8oKCiAUqnEzp07kZqayvTaSqUSGzZswMyZM9G5c2eoVCqsX78egYGBGDNmDLNxrl692ujnmuJSpaysLERERECtViM0NJTfl0tIQr0Ozp8/D4VCgcOHD8PKygrHjx+vUwdCHk/TMn7fvn1wc3Nj3jJeDE5OTkhPT8fRo0cRHR3N75fn6urKfFN38nSuXLkiSg2gWPXThAA1S1NHjhyJnJwczJs3j2/AQwgLlMyRZiUnJwdqtRrx8fEoLCzECy+8gBEjRsDAwAA9evSQOjytJZfLYWNjA2trawB1Oxg2xZlGjQsXLkChUODQoUMwMzPDzp07pQ6pSTl16hQUCgWWLVsmdShP5ZdffsGqVavw8OFD/PTTT+jVqxfy8vIwe/Zs/Pbbb1KHRwCEhIRg27ZtgtUABgcHo1OnTggNDa1TP/3PP/8wrZ/WNQsXLoS7uzvc3d35cykpKUhOTsbixYsljEx7aR4YhYeHo2/fvliyZAlOnz6NiIgI2NnZSR0e0QGUzJFmZdCgQdixYwfatWvHnysqKsLYsWNx4MABCSPTbro209iQvLw8vPLKK1KHobXOnj2LFi1aoEePHli5ciVu374NAPDz80OfPn0kjo6NY8eO1akPJeKrXQMYHh4uWA3ggAEDGpzxd3NzQ0pKiiBj6gJnZ2dkZGTUO0+z2o1bt24dgoOD69SBnz9/HqGhoYiPj5cwMqIrhOs0QYgWqqysrJPIAUC7du1QUVEhUURNg6WlJUxNTZGeno7Y2Fikp6fD1NS0SSZyGzZs4G86+vXrB0dHR/Tp0wd79uyRODLtFhoaio4dOwIA4uPjMXz4cHh5eSE8PFzawJ7RsWPHMHv2bLi7u1MdlRaYNWsWIiMj8cUXXwjazEVTP12bEPXTusbQ0LDB8/r6+iJH0nQMGzaMT+TKysoAAN27d2+0SzQhT4uSOdKs6Onp4e7du3XOFRcXC9Y5UVekpqbC1dUVubm5MDU1RW5uLvr374/Dhw9LHdpT2759O1xdXQEAxsbGOH78OP744w/aluAJysrK+Pqlt99+G/3798fAgQObZHvtzMxMhISEwNPTEwkJCcjKykJycjI2bNggdWjNXkJCgijNXD766CMMGTIEu3fvxsmTJ7F7926MGDECM2bMEHzspszW1rbeljHffvstbG1tJYpI+02ePJk/rr3XLMuNyUnzRt0sSbMSHh6OoUOHwt/fH506dYJKpcKOHTuwatUqqUPTagsWLEBiYiLMzc35c9OmTYOPjw/S0tIkjOzp1e5kGh0dDaCmBrCqqkqqkJoEjuP42qU5c+YAAKqqqlBeXi5xZE/Pw8MDH3zwAZRKJUxNTZnvx0a0n42NDdasWYP4+HgkJCTghRdeQGRkJDU/eYLIyEgsXbqUX44sl8vh6emJyMhIiSPTXrVnexs7JuRZUDJHmpU333wTu3fvxt69e3HhwgV06tQJu3fvFrwNdlMnl8vrJHIAYG5u3iRnNI2NjZGfn48uXbrw+/5cvXq10eVDpMZ7772H999/H59++ik6deqEgoICrFq1Cu+9957UoT2169evY9euXQgMDATHcbh27RqKi4vpfaAZ+eijj7Bjx446+2RS/fSTGRoaYtmyZY02PYqIiKBOjY+4f/8+zp07h+rqapSVldU5JoQFaoBCCHkif39/jB8/nt9vDgAOHjyIzZs3Y/v27RJG9vRycnIwefJkuLi48LOzmZmZ2LRpE3U0fYLExERs376d7wQ7duxYeHh4SB3WMyktLcWuXbsQFxeHwsLCBps7EN1DDVCE4eHhwXS/OV0wceLERj9HdbqEBUrmCCFPdOfOHcyYMQMXLlyAubk5bty4gVdffRXr1q0TtEmBUCorK5GZmYnCwkJ06tQJffv2pQL+ZmTHjh2IiIhAy5YtMXHiRKxevRpqtRrjxo2rM1NDdNfAgQOxc+fOOu9fxcXFGDNmDBITEyWMrGmjZI4Q8VEyRwh5Is3+QVVVVbh16xbat28PPT09yGSyJnfz+7gav/79+4sYSdPi5ORUZ39BoCYpLioqwqVLlySK6r9xdHREamoq7t+/D1tbW1y+fBkGBgZwcnLCsWPHpA6PiODw4cP49NNPG6yfdnFxkTq8Jsvd3R3JyclSh6FVJk6cWO+9E6ip1d60aZMEERFdQzVzhJAn6tevH38sk8mgUqkQHR2N6urqJpfMJSQk8McymQwcx2Hv3r04f/58k2zmIZajR4/yx6WlpdiwYQN27NiBoKAgCaP6b1q1agVjY2MYGxvD2toaRkZGAICWLVtKHBkRC9VPC8PJyUnqELTOp59+WufjrKwsREREoEuXLhJFRHQNzcwRQv613NxcREZG4vLly/j4448xYsQIqUP6z6qqqvDzzz8jJiYGTk5OmDVrFjp37ix1WFrt5s2biI6ORlJSEiZPnoyAgIAm2f2vY8eOcHNzA8dxSE1N5Y/T0tJw/fp1qcMjRGuVlJRg/fr1yMjIQHFxMdq2bQsXFxcEBwfjueeekzo8rXbw4EGsXr0aHTp0QGhoKOzs7KQOiegISuYIIU+keZKoVqsRGhoKZ2dnqUP6z8rLy/Hdd99h69ateOuttxAcHAwzMzOpw9J6wcHBOHHiBKZPn45Ro0ZBLv+/bUpNTEwkjOzpXb16tdHPWVpaihgJIU3LyJEjMXbsWHh5ecHU1BR37tzB/v378csvv2DXrl1Sh6eVFAoF1q9fDwcHB4SEhODFF1+UOiSiYyiZI4Q8kVwuh42NDaytreut/Y+NjZUoqv+mQ4cOsLCwwPjx42FkZFTn+wkODpYwMu3m7u7OH2t+Zpp956jhASHNg6urK9LT0//1eVLz9/Oll15C27Zt67130h6XhAWqmSOEPNHly5elDoGZyMjIBovRyeNRUwNCyJgxYzBo0CC4ubmhdevWuHv3LlJTU+Hj4yN1aFqrurpa6hCIjqOZOUIIIU8UGhraaBIcEREhcjSEEKncunULWVlZuHPnDtq2bYs+ffqgffv2Uoeltfbt29fo54YOHSpiJERX0cwcIaRZeeONN+olJbTk5cmGDx8udQiEEImpVCp07twZXl5eUCgUOHr0KK5fvw5/f3/o6dEtZUOysrIaPC+TySiZI0zQzBwhpFmpqKhokh0YpTZ16lT4+vrCzc2NlqkS0ky5ubkhJSUFM2fOhImJCdzd3XHy5En8+eef2LFjh9ThEdIsyZ/8TwghRHe8/vrrGD9+PPbs2YPKykqpw2kyfHx8oFAo4ODggKlTpyIpKYlqQQhpZjSzb2fOnMHnn3+OwYMHY86cObhx44bEkWmvDRs28M1h+vXrB0dHR/Tp0wdffvmlxJERXUEzc4SQZuf48eNQKpU4ePAgbG1t4ePjAy8vL5qx+xc4jsPhw4ehVCpx+PBh9OvXD19//bXUYRFCRPDll18iPz8fDx48gL6+PgYOHIjs7Gzk5+fj22+/lTo8rdS/f3+kpaUBqOkKnJycDI7j4OXlhQMHDkgcHdEFtMCZENLsODo6wtHREQBw4sQJzJkzBwEBAbhz547EkWk/mUwGMzMzmJmZQV9fH/fu3ZM6JEKISGbOnIk///wT8fHxuH79Oo4cOQIXFxfMmzdP6tC0Vu1awujoaAA176NVVVVShUR0DCVzhJBmKSMjA0qlEikpKejTpw/CwsKkDkmrnThxAgqFAgcOHICNjQ18fHwwb948GBoaSh0aIUREvXv3Ru/evRv8nL+/P7Zv3y5yRNrN2NgY+fn56NKlC+zt7QEAV69epfdOwgwtsySENCvTp09HWloanJycMGbMGHh4eKBFixZSh6X1xo0bBz8/P3h5edFNCCGkQZplhOT/5OTkYPLkyXBxcUGnTp2gUqmQmZmJTZs2oUePHlKHR3QAJXOEkGbl0KFD8PDwgFxO/Z+ext69exvtYknttQkhAODh4YGkpCSpw9A6lZWVyMzMRGFhITp16oS+fftCX19f6rCIjqBlloSQZuWbb76pV6iv2WcuNjZWoqi03x9//NHg+aioKJSUlIgcDSFEG9H8QH0xMTEIDg7Gm2++iZ07d8LV1RUAEBERQcv7CRM0M0cIaVauXr3a6OcsLS1FjEQ3eHp64uDBg1KHQQiRQFlZGQDAxMQEABAbGwtfX18pQ9I6tWcrGzsm5FnQOiNCSLNiaWnZ4P8CAwOlDq1Jog3ECWk+goKCcPfuXQDA999/D1dXV3h6euKrr74CAErkCJEALbMkhBDQ8qAn8fHxqZe4cRyH06dPSxQRIURsZ8+ehampKQDg888/R3Z2NkxMTODq6opp06ZJHJ12ysvLQ1hYGDiOq3N88eJFqUMjOoKSOUJIs6JZFlQbx3Gorq6WIJqmIyoqSuoQCCESU6vVAIDs7Gx0794dLVu2BADqCPwYW7du5Y+HDx/e4DEhz4KSOUJIszJs2DDIZDKaiXtKVE9ICPHz84OTkxNu376Nr7/+GgCgUqlgYGAgcWTaa8CAAXyTLQDIz8/HnTt3YGtrK3FkRFdQAxRCCCGEEPKvlJaWQi6X801PKioqUFFRgVatWkkcmXaKjo7G999/DwMDAwwbNgyJiYkwNjaGlZUV1q9fL3V4RAfQzBwhpFlZsmRJg+dlMhkWLFggcjSEENJ0/O9//8PmzZvx3HPPwcfHB/PmzUNxcTHmzZuH3r17Sx2eVvr5559x6tQpPHz4ENbW1rh48SLkcjmcnZ2lDo3oCErmCCHNSt++ffljmUwGlUqF6OhoVFdXUzJHCCGPMXbsWEyaNAklJSV44403sGHDBrRu3RrTp09HRkaG1OFppZYtW0Imk8HIyAgvvfQS5PKaRvJGRkYSR0Z0BS2zJIQ0S7m5uYiMjMTly5fx8ccfY8SIEVKHRAghWs3d3R3JyckAABcXFxw5cqTeeVJX69at0aNHD3Ach9zcXP74/Pnz/DYPhDwLmpkjhDQrWVlZiIiIgFqtRmhoKC11IYSQf6moqAj79u0Dx3EoKSnhj2/fvi11aFrr3r17j/38X3/9BTs7O5GiIbqIZuYIIc2KXC6HjY0NrK2tAdTd9Do2NlaqsAghROstXrwYMpkMVVVVuHXrFtq3b48WLVqA4zgsWrRI6vCaJA8PDyQlJUkdBmnCaGaOENKsXL58WeoQCCGkSZo5cyZmzJiBo0ePomPHjrh58yacnZ2xdu1aqUNrsmhOhTwrudQBEEKImCwtLWFqaor09HTExsYiPT0dpqamtI8aIYQ8QXBwMIYMGYK8vDxkZGTgwoULGDJkCIKCgqQOrcmqvTqEkP+CkjlCSLOSmpoKV1dX5ObmwtTUFLm5uejfvz8OHz4sdWiEEKLVrl27hnHjxtU5N3bsWBQWFkoUUdNHM3PkWdEyS0JIs7JgwQIkJibC3NycPzdt2jT4+PggLS1NwsgIIUT73bt3D61bt67zMWlcQUEBOnToAAMDA5SXl2Pr1q2orq5GQEAATExMEB8fL3WIpImjmTlCSLMil8vrJHIAYG5ujhYtWkgUESGENA3h4eEYPHgw1q1bh19//RXr1q2Dt7c3lixZInVoWsvHx4efffvggw9QUFCAqqoqvPfeewBQJzEm5L+gZI4Q0qx06tQJ+/fvr3Pu4MGDsLCwkCgiQghpGtzc3JCQkAAzMzNcuHABbdq0wZ49e9C/f3+pQ9NaBgYGMDQ0RFlZGbKyshAeHo7g4GDcuXNH6tCIjqBlloSQZiUmJgYzZszAokWLYG5ujhs3buDVV1/F119/LXVohBCi9dq0aYOAgACpw2gyOI7DuXPnkJycDE9PT/78/fv3JYyK6BLaZ44Q0qwsXrwYAOrsk6SnpweZTIYFCxZIHB0hhBBdcvr0aSxduhQtW7ZEZGQk2rdvj7y8PPzyyy+YP3++1OERHUDJHCGkWam9xFImk0GlUiE6OhrV1dU4c+aMhJERQghpDqqqqhAXFwdfX1+pQyE6gGrmCCHNipeXF7y8vNC1a1fExsbip59+wvLlyymRI4QQwtzJkyfh6+sLX19fnDlzBhs3boSjoyNOnToldWhER1DNHCGkWcnKykJERATUajVCQ0Ph7OwsdUiEEEJ01LRp07B69WoUFxfDw8MDCxYswLFjx6CnR7fghA1aZkkIaVbkcjlsbGxgbW0NmUxW53OxsbESRUUIIUQXeXh4ICkpCQDQr18/ZGZmShwR0TWUzBFCmpWrV682+jlLS0sRIyGEEKLrOnbsCDc3N3Ach9TUVP5YJpPRA0TCBCVzhBBCCCGECKCxB4gymQwvvviiyNEQXUTJHCGEEEIIISIoLi7Gr7/+CqVSiX379kkdDtEB1M2SEEIIIYQQgRQXF2PTpk1455130KtXLxgaGmLTpk1Sh0V0BM3MEUIIIYQQIoAhQ4bAxMQEvr6+GDlyJEaPHo2EhASpwyI6hGbmCCGEEEIIEUCvXr1w7949nDx5En///Xe9LsqEPCuamSOEEEIIIURA2dnZUCgU2LdvH9zc3ODu7o6RI0dKHRbRAZTMEUIIIYQQIpJTp05BqVRi6dKlUodCdAAtsySEEEIIIUQACxcu5I9jYmIAAPb29rh//75UIREdQ8kcIYQQQgghAkhPT+ePlUolf5ydnS1FOEQHUTJHCCGEEEIIIU0Q1cwRQgghhBAigI4dO8LNzQ0cxyE1NZU/TktLw/Xr16UOj+gAPakDIIQQQgghRBfZ29tj7ty5aNOmjdShEB1FM3OEEEIIIYQI4MqVKwgLC0Pbtm2xYsUKtG3bVuqQiI6hZI4QQgghhBABhYSEYNu2bbC0tATHcZDJZDh+/LjUYREdQMssCSGEEEIIEcDff/+NkJAQdO/eHefPn4epqanUIREdQzNzhBBCCCGECMDb2xurV6+GjY2N1KEQHUXJHCGEEEIIIYQ0QbTPHCGEEEIIIYQ0QZTMEUIIIYQQQkgTRMkcIYQQQgghhDRBlMwRQgghhBBCSBNEyRwhhBBBdO3aFT169ICDgwMcHBwQFBT0TNcLDw9HRUUFo+ie3pYtW3D+/Pn/9LUymQx2dnawt7eHjY0NNm/ezDg6QgghzRF1sySEECKIrl27Ys+ePejZsyeT68lkMpSUlKBVq1ZP9XXV1dUAALn82Z5furm5Yfbs2Rg+fPhTf23t2E+fPo3XX38dV65cgYWFxTPFRAghpHmjmTlCCCGiKikpQWBgIBwdHWFnZ4egoCBUVlYCANasWYM33ngDvXr1gqOjI44dOwYA/Kyes7MzHBwccOPGDUyYMAHr16/nrzt79myEh4cDqJnFCwgIwDvvvAMHBwcUFhbiwoULGDZsGN544w3Y29sjJiYGAPDgwQP4+fnBxsYG9vb2GDx4cL2Yv/vuO/zxxx/46KOP4ODggH379qGqqgqzZ89Gz5490bNnT8yYMeNfzRza2trCzMwMKpWKPxcVFQVHR0f07t0bQ4cORX5+Pv99jB07FsOHD8crr7wCX19fnDx5Eh4eHnj55ZfxySef8NfIy8vDoEGDYGdnBwcHB/z2228AgGXLlmHGjBn8vystLUXbtm1x69atx45NCCGkCeAIIYQQAVhaWnJWVlacvb09Z29vz+3cuZPjOI4LDAzkfvzxR47jOK66upqbNGkSt2bNGo7jOO7GjRv81x89epR77bXX+I8BcCUlJfzH48eP59atW8d/HBISwi1atIjjOI5btGgR98ILL3DXr1/nOI7j1Go19/rrr3Pnzp3jOI7j7t+/z9na2nInTpzgdu7cyXl6evLXKSoqavD7GTBgALd7927+45iYGM7NzY0rLy/nKisrOW9vby4iIqLBr60de0pKCmdjY8M9fPiQ4ziO++mnn7jAwEBOrVZzHMdxP/74Izdy5Ej++3jllVe4O3fucGq1mrOzs+MGDx7MlZeXc6WlpVyHDh243NxcjuM4ztHRkfvmm284juO48+fPc23btuX++ecfLj8/n+vQoQM/3qZNm7h33nnniWMTQgjRfnpSJ5OEEEJ0l1KprLfM8rfffkNmZiZWr14NoGZmzMDAAABw8uRJLF++HEVFRdDT00NOTg4qKir4zz+N4cOHo2PHjgCA3NxcnD17Fu+++y7/+ZKSEuTk5MDZ2Rl///03goODMWDAAAwdOvRfXf/QoUOYNGkSDA0NAQCBgYHYsGEDQkNDG/z3zs7OKCsrw+XLl6FQKPjv6bfffsMff/yBPn36AACqqqrQokUL/uu8vLxgamoKAHzdnaGhIQwNDWFlZYVLly7BwsIC2dnZmDRpEgDg1VdfhaurK9LT0zF27Fj06tULu3btwpgxY7BlyxaEhYX9q7EJIYRoN0rmCCGEiIrjOPz22294+eWX65yvqKjA6NGjkZKSgj59+uDevXswNTVtNJnT09NDVVUV/3F5eXmderraxxzHoX379sjOzm4wppycHCQlJeHQoUMICwtDdnY22rRp88TvQyaT1Tn36Me1ZWRkoFWrVtiyZQsmTpwIFxcXmJubg+M4zJ8/Hx988EGDX2dkZMQft2jRot7HarUa3P8vf28snokTJ2LLli1wcHBAXl4evL29+e/hcWMTQgjRblQzRwghRFQjR47EypUroVarAQDFxcXIy8tDeXk5Kisr0aVLFwDAunXr6nzdc889h7t37/Ifd+vWja+pKyoqwr59+xod08rKCiYmJvjxxx/5c3l5ebh9+zZUKhVkMhlGjhyJqKgocBzXYN1Y69at64zv6emJLVu2oKKiAmq1Gps2bcKgQYOe+P1PmDABAwcOxIoVK/ifR0xMDG7fvg0AqKysxMmTJ594nUdjc3BwwA8//AAAuHjxIo4cOQIXFxcAwKhRo3D8+HGsXLkSAQEB/Owbi7EJIYRIh5I5QgghooqOjoaenh4cHBxgZ2eHQYMG4cqVK2jdujWWLFkCR0dH9O/fn1++qBESEgIPDw++AcqHH36I//3vf7C1tcWkSZPQt2/fRsfU09PD7t27ERsbCzs7O7z22muYPHkyHjx4gNOnT8PZ2Rl2dnbo3bs3AgICYGdnV+8aU6ZMwZIlS/gGKFOmTIG9vT169+4NBwcHdO3aFR999NG/+hmsWrUKmzdvRkFBAQICAvDee+/Bzc0N9vb2cHBwQHJy8tP9UAH89NNP2LZtG+zt7TF69Gh89913fGJsaGgIHx8fbNq0CRMnTuS/htXYhBBCpEFbExBCCCGEEEJIE0Qzc4QQQgghhBDSBFEyRwghhBBCCCFNECVzhBBCCCGEENIEUTJHCCGEEEIIIU0QJXOEEEIIIYQQ0gRRMkcIIYQQQgghTRAlc4QQQgghhBDSBFEyRwghhBBCCCFNECVzhBBCCCGEENIEUTJHCCGEEEIIIU0QJXOEEEIIIYQQ0gT9P3k0YfJ4XbsdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f391d0f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 features with a correlation greater than 0.98.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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nf2UcvLy86NGjB0ePHmXFihUAfPHFF2b5lXL3O3v2bL7vidtFfu9ruHxbk/yO48r7QoaEhDB9+nROnDjB1q1befvttzEMg8GDBzNjxoy/3MeiRYv+z+/b3Nf/Wu8TEZGbSQmjiMgtFBsbi6enJx999BEnTpzIt27u7FvVqlXx9fVl8+bNLm9fkJycDFz7aqI3okmTJqSlpZlL9P6KX3/9FYAHH3zQadvVt2vI5eHhcd237ADMpZR2u91p2/nz59myZQt+fn7XPZubH4vFQrVq1Rg8eDBLly4F4JtvvrnmfrnLetu3b8+AAQOcHrlX5HS1/Hfr1q0uZ3JzZ8fq1KkD/Pe1X7VqlVPiZhiGOd5Xv0ceeeQR4HKieODAAdatW0dUVBRBQUEO9Zo0aQL8d2nqnapRo0ZYLBaHZcfXy9PTk7p16/LCCy+YieL1vP55qVevHhcuXGDTpk1O2673M537+rv6PP3222+6tYaI3BJKGEVEbqHKlSvzwgsvcPLkSdq3b+9ylu7ixYu8++675jlWhQoVomfPnpw8eZKxY8c61F22bBmLFi2icuXK5uzlzZB7Of/+/fuTlpbmtP3YsWP88ssv+bYRHBwM4HRvu+TkZD7++GOX+xQvXpzU1NTr7mfz5s2pVKkSixYtcpqhGzt2LCdPnqRnz55/+XYPBw4ccHnPw9yZGz8/v3z3//PPP5k1axb+/v7MmjWLTz75xOmRmJhIqVKlmDdvntNY//HHH7z11lsOZf/5z3/46aefiIyMNJc03nPPPbRq1cq8jcaVPvvsM3bs2OFQP1fuuYpz5szh448/xjAMp+WoAIMGDcLLy4unn37aZRJy+vRp8zzK21mZMmXo1q0b69atY/z48S5nRTdu3Gj+x8zPP//Mb7/95lTnel///PTt2xeA+Ph4Ll26ZJYfPnyYd999Fy8vL6clzFcLDw+nYsWKLFiwwOFzZhgGL7744g3954uIyPXSOYwiIrfYG2+8wcWLF5k4cSKhoaFERkZSs2ZNvL29OXDgAMuWLSMtLY033njD3Oftt98mOTmZN954g3Xr1tGkSRPzPoyFCxc2z5G7Wdq1a8fLL7/M66+/TuXKlWnXrh3BwcGkpaWxb98+Vq9ezRtvvOFwH8CrRUdHExISwrhx4/j555+pWbMmu3fvZsGCBTz00EPMnj3baZ/IyEhmzZpF165dqVevHp6enjzwwAMuL2ACl2ckp02bRlRUFB06dCAmJobg4GA2btzIihUrqFSpklPCdSN+/PFHOnfuTKNGjahZsyZlypTh8OHDzJs3D09PT/Ocxrx8/fXXnDt3jn79+lGkSBGXdby8vHjkkUd49913+eKLLxg6dKi5rUWLFrz33nts2LCBRo0asWfPHubOnUtgYCDvv/++QzuTJ08mPDycxx57jG+//Zbq1auzc+dOvvnmG4KCgpg8ebLL+L1792bkyJFMmDCBokWLupwRrlmzJh988AFPPvkkoaGhdOjQgUqVKnH27Fn2799PcnIysbGxTJky5VpDWuA++OADdu/ezQsvvMDnn39O06ZNCQwM5NChQ3z//ffs3buXo0ePUrhwYZYtW8awYcNo3rw5VatWpUSJEuzfv59vvvkGPz8/nnrqqb/cj969ezNnzhzmz59P7dq16dixo3kfxrS0NN555x3uvffefNvw8PDgo48+okOHDrRp08a8D+OKFSs4evQotWvXZvv27X+5jyIiLrnpaqwiIv94mzdvNvr3729UrlzZ8PPzM3x8fIyQkBCjZ8+expIlS5zqnzhxwhgyZIgRHBxseHt7GyVLljS6du1q/PTTT051cy/jf+DAAZexc2+rsXLlynz7uHTpUiM6OtoICgoyvL29jTJlyhhNmzY1Xn/9dYdbNOR3H8YuXboYQUFBRuHChY1GjRoZX3/9dZ71jx49anTr1s0oWbKk4eHhYQDG1KlT841hGJdvf9G1a1ejZMmShre3txEcHGwMGTLEOHHihFPd4OBgIzg42OXxRkREONyG4NChQ0ZcXJwRFhZmlCpVyihUqJBxzz33GF27dnV5O4SrhYWF5Xk7iiv99NNPBmDUqlXLMIz/3jKib9++xvbt24127doZAQEBRpEiRYwHHnjA+Pnnn122k5KSYvTr188oW7as4eXlZZQtW9bo16+f0207rt7HYrEYgNGvX798+7lp0yajR48eRrly5cz3YP369Y24uDjjl19+ucZoOLve22r07dvX5XZc3IYlv9c31/nz541x48YZDRo0MPz9/Q0/Pz+jYsWKxkMPPWT85z//MS5dumQYhmHs3LnTGDp0qFGvXj2jRIkSho+Pj3HvvfcasbGxxs6dOx3azO8zl9fn7dKlS8aECROMWrVqGT4+PkZAQIARERFhzJ8/36mN/N7/q1atMlq2bGn4+fkZxYsXN2JiYozffvvN6f0sInIzWAzjNjprXURERERERG4bOodRREREREREXFLCKCIiIiIiIi4pYRQRERERERGXlDCKiIiIiIiIS0oYRURERERExCUljCIiIiIiIuKSEkYRERERERFxyaugOyBys6Snp7s9ZkBAAJm/HXJrzELBFcj49YBbYwL4VKpI1vETbo3pVTqowF5Xd8cNCAggM+WgW2MCFAq5h8yDqe6NeU95AC7+stutcX2rhZKx91e3xvSpUgmArBMn3RrXK6gkl1IPuzWmd/m7ATg17Su3xi0e28vt34k+lSoCBXOsBfV5LYjvxIKICXBx1x63xvWteh97w6PcGrPKmsUAXDp81K1xve8u69Z4cnNohlFERERERERcUsIoIiIiIiIiLilhFBEREREREZeUMIqIiIiIiIhLShhFRERERETEJSWMIiIiIiIi4pISRhEREREREXFJCaOIiIiIiIi4pIRRREREREREXFLCKCIiIiIiIi65NWFs164dO3fuBODAgQO0adOGoKAgrFYrVquVkSNHmnXj4+OJiooyn6ekpJh1w8LC+P777wHYvn07LVu2JCIigrCwMA4dOuQydkBAAFarlUaNGvHuu+8CYLfbqVChghn//fffB2Dnzp106NABq9VKeHg4H3zwAQCjR49mwYIFAFgsFmbOnAnArl27iI2NBSA2NpZGjRqZbe7bt4/33nuPiRMnArB//35at27tULdJkyaMGjXKLPv5558djrtr167mc5vNxujRo12OU2Zmphk393itVitnzpwBoFmzZrz55psAbNq0yRzL3HHt168f06ZNM8fh0KFDREdHExERQWRkJD/99JM5boUKFSI1NRWAKVOmMG3aNNcvOvDhhx8SGhpqPu/VqxebN282ny9fvpwnnngCgPnz5xMREYHVaiUyMpLk5OQ82xURERERkVvLy53B3nnnHYYPH87ChQt54YUXGDduHG+++SZJSUlOdTdt2kRAQABpaWmUKFECgIiICJKSkli3bh1vvfUWiYmJvP7660yePJkaNWpw4cIFLBaLy9ihoaHY7XZycnKoU6cOQ4cOBaB79+5MmDDBrJeZmcmAAQOYMWMGISEhGIbB6tWrndqrWLEi77//Pt27d3faNnXqVGrWrGk+HzRoEC1btuT//u//eP755xk/frxD3erVq9OgQQMee+yx6xzJvMfJbrcD0LBhQ/NnuJz8BQcHs3z5cl588UUaN26M3W4nJSWF4cOHm6/BlYlf3759mThxInXq1GHXrl3ExMTwww8/AHDffffx7rvvmsl3fhYsWEDTpk358ccfqVOnDt26dSMpKYlGjRoBkJSURExMDLt27eLf//43ixYtonDhwvz555/s3r3bZZsZGRlkZGQ4lGVmZuLj43O9QyciIiIiItfg1hnGGjVqUKVKFZ577jmKFStG8eLFXdbbunUr9evXp1u3bsydO9dp++nTpzEMA4DChQuzcuVKzp07h5+fH76+vvn2ITMzE29vbzw8XB/6hg0baNasGSEhIcDlmcSWLVs61StevDh169ZlyZIl+cYD8PLyYvTo0Tz44IMUL16c+vXrO2z38PCgatWqHD58+JptXela43SlpKQkHnnkESpVqsS+ffuu2fbBgwcJDAykTp06AFStWpU6deqwYcMG4PJs8YYNGzh16lS+7Zw8eZIiRYrw+OOPk5iYaO6bO245OTmsWbMGq9XKrFmzePrppylcuDAARYoUoUGDBi7bHTt2LIGBgQ6Pd95555rHJSIiIiIi18/t5zC++uqrfPHFF4wZMwaA5ORkpyWhiYmJdO/enU6dOvHdd9+Z+yYnJ9OkSRP69OnDK6+8AsD48ePZsWMHderUoUePHpw/f95l3N27d2O1WgkNDeWBBx4wZyJnzpxpxk9KSuLo0aOUK1cOgG3btmG1WmncuLHLNocNG+YySenXr5/Z5p9//glAWFgYO3bsoEePHk71L1y4wPbt27n33nuvawxz5TVOrixfvpy2bdvSs2dPM3HLz9GjRylfvrxDWYUKFThy5Ij5fNCgQfz73//Ot505c+bQpUsXmjZtai5D9fX1pVq1amzbto3Vq1fTrFkzPD09HcZ+2rRphIeHM2jQIJftxsfHc+bMGYfHsGHDrnlcIiIiIiJy/dy6JBXgrrvu4p577iEoKIhz586Zy0yvtHDhQnPp486dO0lLSwP+uyQ1ISGBDRs2ULt2bUqVKsXkyZMBGDVqFJ9//jkDBw50ipu7JDUrK4uIiAgeffRRwHlJ6urVq9m4cSMAdevWxW6307BhQ5fHEhISQsmSJR3OxwPnJakAr732Gi+99BJjx441z2GEy8mln58fzz77LEFBQU4xfH19HZZeXrx4ET8/vzzHKXf57pVSU1PZvn070dHR5OTkcO7cOeLj410eU65y5co5zXimpqbSoUMHsrOzAejRowdNmzalZ8+eec7szp8/n4yMDD755BP27t3L9u3bqV27NjExMcyePZvTp08TExPjELNBgwbExsZitVoZPny4y3Z9fHyclp+mp6fne0wiIiIiInJjbrurpG7bto2IiAhsNhs2m40xY8Ywb948hzqDBg1iypQpXLp0ib1795rlpUqVIicnJ9/2vby88PPz4/Tp0y63N2nShPXr15OSkgKAYRhmguRK7rmY+dmzZw/bt28nLi6OunXrMmvWLHPb1KlTWbVqlZnAXq106dIcOnTInDlds2YNtWrVuq5xypWbZNtsNpYsWULVqlWvuSy1QoUKnDlzxrzQzZ49e/jxxx8JCwsz63h5edG7d2+mTp3qso2TJ0/i6+vLsmXLsNlsTJ8+3ZzdbN++PTabjVWrVmG1WgHo1q0b//rXv8xjzcrKyrePIiIiIiJya7l9hvFquUtS4fKMnr+/P61atTK3t27dmv79+zvMynl7exMVFcXs2bPZu3cv3377LYULF+auu+7iiy++cBknd0lqRkYG9erVo06dOtjtdmbOnMmWLVuAy+fWxcXF8emnn/Lkk09y8eJFPD096d27d579r1OnjtPSzX79+uHv7w/ApEmTePnll82k8uWXX6ZNmzZER0fn2eZjjz1m7v/ll1/y+uuv06ZNGwoVKkT16tVp3749I0eOdDlOAwYMcGpv9uzZzJ8/36FuYmLiNWcZp06dyuDBgzl79iyenp58+eWXeHt7O9R59NFHee2111zuP3fuXFq0aGE+DwsL45lnnuH111/H19eXypUrExAQgJfX5bdhaGgogwcPpl27dnh7e+Pl5cVzzz2Xbx9FREREROTWsRi5V48RucMVxJLUgIAAMn9zfSuXW6VQcAUyfj3g1pgAPpUqknX8hFtjepUOKrDX1d1xAwICyEw56NaYAIVC7iHzYKp7Y95z+T/ZLv7i+irIt4pvtVAy9v7q1pg+VSoBkHXipFvjegWV5FLqjV1I7X/lXf5uAE5N+8qtcYvH9nL7d6JPpYpAwRxrQX1eC+I7sSBiAlzctcetcX2r3sfe8KhrV7yJqqxZDMClw0fdGtf77rJujSc3R4HPMN5sZ86coVOnTg5lkyZNom7dugXUo3+Ofv36ceDAf39px8bGmvenFBERERGRO8/fLmEMDAx0uP+guE9e5zKKiIiIiMid6ba76I2IiIiIiIjcHpQwioiIiIiIiEtKGEVERERERMQlJYwiIiIiIiLikm6rISIiIiIiIi5phlFPr+taAAAgAElEQVRERERERERc+tvdVkP+uS4dO+72mN5lSvP7hH+5NWap4U+7PWZu3FOfz3RrzOK9u5O+ZIVbYwIEtI3k3PpNbo3p37Qxv7br4taYAJVssznwcG+3xqw453MATk2f4da4xfv2ZG/LDm6NWWXVQgB+f+d9t8YtNewpjr0+zq0xy7z8AgBnFyx2a9yiHaMK5HsYCuZYT0z8t1tjBj07GID09HS3xg0ICCiQmECB/K7L2PurW2P6VKkEwMF+g9wa956pH7g1ntwcmmEUERERERERl5QwioiIiIiIiEtKGEVERERERMQlJYwiIiIiIiLikhJGERERERERcUkJo4iIiIiIiLikhFFERERERERcUsIoIiIiIiIiLilhFBEREREREZf+0QljSkoKXbt2NZ+PHj2aBQsW0KtXLzZv3myWL1++nCeeeAK73c7w4cMBsFqtPPnkk2adsLAw8+ePPvqIli1bEhERQbt27fjpp5/MbfHx8URFRTn0ISgoCKvVSvPmzdm3b5+5rVmzZrz55pvm82nTplGlShWsViv3338/p0+f5qOPPsJqtVK1alVzW0JCArGxsVitVnPfcePGERQU5NSO1WolKSmJlJQULBYLGzduBMBmszF69Ghee+01rFYrISEh1KpVC6vVysyZM5k4cSJhYWE0b96cxx9/PM8xPnv2LH5+fqxbtw6AJUuWMHjwYIc6tWrVIiMjg9TUVLp27YrVaiU8PJxXXnklz3ZFREREROTW+0cnjHnp1q0bSUlJ5vOkpCRiYmKc6m3ZsoXjx487lC1btozk5GRWrFhBcnIyX3zxBZ6enub2TZs24efnR1pamlkWERGB3W7nueee4+233wbg0KFDBAcHs3z5cof2hw4dit1up1WrVnz55Zc8/vjj2O124uLizG1Dhw4F4OLFi2b/1q5dS4UKFZzasdvtZtJcvXp1xo0b5xDvlVdewW63Exsby9ixY7Hb7XTo0IGZM2eyfv161q5da/bZlW+++YZ+/fqRmJgIQGRkJKtWrSInJweAjRs3UrduXXx8fIiNjWXkyJHY7XbWrFlD69at82w3IyODs2fPOjwyMjLyrC8iIiIiIjdOCaML7dq1Y8mSJQDk5OSwZs0ah9m6XE899RQJCQkOZV999RVxcXF4eXkBULJkSapXrw7A1q1bqV+/Pt26dWPu3LlO7dWsWZPU1FTgcpL6yCOPUKlSJYdZx1ynT5/GMIx8j6Nz587MmzePgwcPUr58eTw88n+5q1WrRlZWFrt27cq3noeHB2lpaWzbtg3DMChWrFiedefPn8+YMWPYvn07hmHg5eVFWFiYOeOYm4wfPHiQwMBA6tata+4bERGRZ7tjx44lMDDQ4fH2v97Lt98iIiIiInJjlDC64OvrS7Vq1di2bRurV6+mWbNmDrOEuTp37szy5ctJT083y44ePUq5cuUAeOuttwgLCzOXlSYmJtK9e3c6derEd99959Te6tWrCQ0NBS4vg23bti09e/Y0Z+cAEhISqFGjBkuXLqV37975Hsf999/PsmXLSEpK4uGHH3bYlpCQYC5JXbNmjVn+/PPPM378+Hzb9ff3Z/Lkybz00ktUrlyZTz75xGW99PR0M6EMDw9nw4YNAMTExJgzuEuXLiUqKsph3I4dO2Yusz1//rzLtuPj4zlz5ozDY8TTQ/Ltt4iIiIiI3Bivgu7A7SomJobZs2dz+vRpl8tR4fJM26OPPsqUKVPMsnLlynH48GFKlChBXFwcYWFhLFiwAICFCxfyww8/ALBz505zWWpycjJWq5XixYszefJkUlNT2b59O9HR0eTk5HDu3Dni4+OBy0tJn3jiCfr06UNqaiqBgYF5HkOhQoXw9/fn22+/ZcgQx2Rq6NChPPXUU+bzlJQUAPPcwcOHD+c7Pm3atKFNmzakp6fTsmVLevToQZEiRRzqfPPNN+zZs4d27dpx7tw50tPTadq0KZGRkYwYMYItW7ZQs2ZNfHx8zHEDKFOmDHa7HavVai5dvZqPjw8+Pj4OZZfOX8i3zyIiIiIicmM0w5iH9u3bY7PZWLVqlcvlqLn69OnDzJkzuXTpEgC9evXi7bffJisrC8D8d9u2bURERGCz2bDZbIwZM4Z58+YB/z2Hcc6cOZQuXZqkpCQSEhKw2WwsWbKEqlWrOixL9fLyIi4ujtdee+2ax9G/f386depkLpG9Hs888wyTJk3Kc/vFixc5dOgQAEWKFMHX19dlvdmzZ7Ny5UpsNhurV6/mhx9+MJelNmjQgBEjRpjJeIUKFTh79ixbt241988dOxERERERKRj/+BnG1atX06ZNGwDWr1/PzJkzgcvLUitXrkxAQEC+yZaPjw8xMTFMnjwZgNatW7Nv3z5atWplzoKNHj2axMREWrVqZe7XunVr+vfv7/LCLrNnz2b+/PkOdRMTEylbtqxZVrt2bY4cOUJqairly5fPs38tW7akZcuWTuUJCQnmstCrr6gaHR1NXFxcnm1eunSJ2NhYMjIyyM7OplevXk6zi+np6Rw5coQSJUqYZffddx8bNmygadOmxMTEEBMT43DF2GnTpjFkyBBOnTqFt7c34eHhFC5cOM9+iIiIiIjIrfWPThhDQkKcrnJ6pRkzZjg8zz3nD8But5vlI0aMYMSIEebzgQMHMnDgQId9GzVq5PC8XLly2Gw2AIcrssLlJPZKvXr1ctm/K+vFxsY6bJs2bZpT/S1btph1r65/ZT8sFgs7d+502DZ69Gjz54CAAKert14tICDAPGcx10cffWT+HBUVxdmzZx22ly9fnjlz5uTbroiIiIiIuM8/OmGUm2fmzJnmLCtcPg/x66+/LsAeiYiIiIjI/0oJo9wU3bt3p3v37gXdDRERERERuYl00RsRERERERFxSQmjiIiIiIiIuKSEUURERERERFxSwigiIiIiIiIuWQzDMAq6EyIiIiIiInL70QyjiIiIiIiIuKTbasjfRnp6uttjBgQEcHHXHrfG9K16H5cOH3VrTADvu8ty6dhx98YsU5rzW7a6NSZA4Yb12Bse5daYVdYs5sK2n9waE8Cvbi0y96e4NWahe0MAyDpx0q1xvYJKcm7tRrfG9G/eBIBLR465Na53uTJu/57wvrssQIF8T1xKPezemOXvBgrmWDN273NrTJ/QyoD7f8cGBAQUSEz4Zx1r5sFUt8YtdE95t8aTm0MzjCIiIiIiIuKSEkYRERERERFxSQmjiIiIiIiIuKSEUURERERERFxSwigiIiIiIiIuKWEUERERERERl5QwioiIiIiIiEtKGEVERERERMQlJYwiIiIiIiLi0h2XMKakpGCxWNi4cSMANpuN0aNH88cff9CzZ08iIiIIDw/HbrfnWx/AarXy559/OrTdtWtXlixZwuDBgx3i1qpVi4yMDBo2bAjAtGnTKFmyJOfPnwcgLi7OjLlz5046dOiA1WolPDycDz74wGxnz549eHp6cvjwYbPMarXSokULGjZsyEcffWSWf/jhh4SGhjr0IyAgAKvVSqNGjVi0aBEHDx7EarVitVrNbe3bt8dut+Ph4cGBAwcAOHbsGF5eXixYsMChHavVyqOPPmr248knnzRjhYWFsWnTJqxWK2FhYQQFBWG1WunXrx/bt2+nZcuWREREEBYWxqFDh/J8zXr16sXjjz8OgGEY1KpVi0uXLpnbX375Zb7++mtycnJ4/fXXiYiIICIigoceeojU1NQ82xURERERkVvLq6A78FdUr16dcePGMXv2bLPs6aefpk+fPrRv355jx47RunVrVq9enWf9/ERGRjJs2DBycnLw8PBg48aN1K1bFx8fH4d6ZcqU4ZNPPmHIkCFmWWZmJgMGDGDGjBmEhIRgGIbZD4BZs2YxcOBAkpKSGDp0qFm+aNEiPD09qVatGv369cPb25sFCxbQtGlTfvzxR+rUqQNAaGgodrudI0eO0KlTJzZv3mwmqg0bNjR/ttvtNGjQgNmzZzN8+HDmzJlD/fr1zXi57Vxty5YtHD9+nNKlSwPQuHFj7HY7KSkpDB8+nKSkJABiYmKYPHkyNWrU4MKFC1gsFpdjefHiRU6fPs3Ro0fJysrCy8uL1q1bs2zZMtq3bw/AwoULGTFiBJ999hnnzp0jOTkZgN9++43s7GyX7WZkZJCRkeFQlpmZ6fQaiYiIiIjIX3fHzTACVKtWjaysLHbt2gVAdnY2e/bsMROQMmXK0LlzZ7777juX9a/Fy8uLsLAw1q1bB0BSUhIxMTFO9WJjY/n888/JysoyyzZs2ECzZs0ICQkBwGKx0LJlS3P7qlWrGD9+PIsXL3Zqz8/Pj7Jly3Lq1ClOnjxJkSJFePzxx0lMTHSqe/r0aQzDyPc4WrRowZo1awBYtmwZbdq0ueaxP/XUUyQkJFyzXuHChVm5ciXnzp3Dz88PX19fl/UWLVrEAw88wP33328mqDExMWbyvmPHDoKDgylSpAgzZszgxRdfNPcNDg4mODjYZbtjx44lMDDQ4fHOO+9cs98iIiIiInL97siEEeD5559n/PjxAJw8edKcEctVoUIFjhw54rL+9YiJiTFn05YuXUpUVJRTHV9fXzp16sSMGTPMsqNHj1KuXDkAtm3bhtVqpXHjxsDl5aihoaH4+/tTunRph2Wpucdx/PhxgoKCmDNnDl26dKFp06Zs3rzZrLN7927Cw8Np0qQJb7/9dr7H4OHhQdmyZdm6dStFixalUKFCDu3kLkkdOXKkWd65c2eWL19Oenp6vm2PHz+eHTt2UKdOHXr06GEuzb3anDlz6Nq1Kz169DDHs1mzZmzZsoWsrCyHZPzs2bMULVoUgGeeeYaGDRvyn//8x2W78fHxnDlzxuExbNiwfPssIiIiIiI35o5NGMPDwzlw4ACHDx+mZMmS/P777w7bU1NTzcTt6vrXIzIykuTkZLZs2ULNmjXzXOo4ePBgPvjgA3O2r1y5cmaMunXrYrfbycnJASAxMZFNmzbRrl07fvrpJ4clsu3bt6dLly4kJCTg4eHB/PnzmTJlCu3bt2fv3r1s374duLyUdM2aNXzxxRcsX778msfx8MMP079/fx566CGH8twlqXa7nTfeeMMs9/Dw4NFHH2XKlCn5tluqVCkmT57Mvn37CA0N5fPPP3eqc/HiRdasWUPfvn0ZNGgQy5cvJzs7G4vFQkREBMnJySxYsICOHTsCULRoUc6ePQvApEmTeOqpp8znV/Px8aFo0aIODy1HFRERERG5ue7YhBEuz0JNmjQJT09PqlSpwpIlSwA4fvw4c+fO5YEHHnBZ/3p4eXnRoEEDRowY4XI5aq5ixYrRtGlTFi5cCECTJk1Yv349KSkpwOWLvOSeh7ds2TLWr1+PzWZj7dq15pJZuLx0Mzk5mejoaE6ePImvry/Lli3DZrMxffp0p2WpnTp1Ijk5mbS0tHyPo1WrVtSuXZt27dpd13ED9OnTh5kzZzpcmOZqe/fuNX8uVaqUmRRfyWazMWTIEGw2GzabjT59+jgsS33zzTcpX748AQEBAPTs2ZMxY8aY+1+51FdERERERNzvjk4Yo6OjzWTsX//6F1OnTsVqtdKlSxfef/99ihcvnmf9K8vatGnj8vy+mJgYNm/e7HI56pWeffZZdu/eDUChQoX49NNPefLJJ2nVqhX3338/vXv3Zu/evQQGBuLhcXnIfXx8sFgsLmc8586dS4sWLcznYWFhZkJ6pT59+vDxxx/n2zcvLy+mT5/udI7hlUtSe/To4bDNx8eHmJiYfJPRr7/+msaNG2O1Wlm6dCm9e/d2qpOYmEirVq3M561bt2bWrFkANG/enN27dzsk4/3796dIkSK0aNGCtm3bsnDhwmuOvYiIiIiI3DoW41pXThG5Q1zrvMtbISAggIu79rg1pm/V+7h0+KhbYwJ4312WS8eOuzdmmdKc37LVrTEBCjesx95w9/5nRZU1i7mw7Se3xgTwq1uLzP0pbo1Z6N4QALJOnHRrXK+gkpxbu9GtMf2bNwHg0pFjbo3rXa6M278nvO8uC1Ag3xOXUq/vdJObFrP83UDBHGvG7n1ujekTWhlw/+/YgICAAokJ/6xjzTzo3tuXFbqnvFvjyc1xR95WQ24/Z86coVOnTg5lkyZNom7dugXUIxERERER+V8pYZSbIjAw0OV9HUVERERE5M51R5/DKCIiIiIiIreOEkYRERERERFxSQmjiIiIiIiIuKSEUURERERERFxSwigiIiIiIiIu6T6MIiIiIiIi4pJuqyF/G7+nn3d7zFIBhf8RN/gtqLg61r9n3H/izbELIm7WiZNujekVVBLQ63qr4xbUsVpHv+/WuPbRT+l1vcUxoWCOVe48WpIqIiIiIiIiLilhFBEREREREZeUMIqIiIiIiIhLShhFRERERETEJSWMIiIiIiIi4pISRhEREREREXFJCaOIiIiIiIi4pIRRREREREREXFLCKCIiIiIiIi4pYRQRERERERGXlDDeJlJSUggKCiIyMpKIiAheeOEFLly4AMDixYsJDAzk4sWLALz++ut8/PHHAGRlZdG8eXNOnDjB8OHDad68OU2bNmXUqFEu44wePZpatWphtVqxWq2sWbMGu92Oh4cHBw4cAODYsWN4eXmxYMECAAICArBarTRq1IiNGzcC0LBhQ6e233vvPVq2bEnz5s0ZOXIkOTk5fP7557z00ktmnQceeIBffvkFq9VKixYtzH78+eefZt8iIyOxWq3Mnz//Jo2uiIiIiIj8FV4F3QH5r4iICJKSkjAMg1deeYXRo0fz9ttvk5iYSK9evVi8eDGdOnXi+eefJyIigpiYGD7//HO6devG77//zm+//cbatWsB+OOPP/KMM3bsWDp27Gg+t9vtNGjQgNmzZzN8+HDmzJlD/fr1ze2hoaHY7Xa+//574uLiWLp0qVObS5cuZf369Wby+eijj/LZZ58xYMAAWrduzf79+9mxYwf33Xcf1apVA2DRokUUKVLEZd/S0tJo27Yt9957L7Vq1XKKl5GRQUZGhmNZZjY+Pj7XMdIiIiIiInI9NMN4G7JYLIwcOZL58+eTlZVFamoqL730ErNnzwbA19eXuLg4nnnmGWbOnMngwYPx9fXl119/Zffu3QAUK1bshmK2aNGCNWvWALBs2TLatGnjVKdmzZqkpqa63H/GjBnExcXh4XH5LRUXF8eMGTOwWCyMHz+eZ599lrfeeivPmc+rlShRgsGDBzNv3jyX28eOHUtgYKDD4713JlxX2yIiIiIicn2UMN6mfHx8yMjIYMWKFbRp04by5cvzxx9/mLNqnTt35sCBA4wYMQIvLy8qVapEXFwcTzzxBKGhoXz77bd5th0fH28uBd23bx8AHh4elC1blq1bt1K0aFEKFSrktN/q1asJDQ112ebRo0cpX768+bxChQocOXIEgAYNGhAYGEiPHj246667zDrt27fHarU6zHZeqVy5cmYbro7hzJkzDo8hw4bnecwiIiIiInLjtCT1NpWZmYmPjw9JSUns3r2bZcuWcejQIRYvXsyDDz4IQMWKFalYsaK5T7du3ejWrRvHjx/n/vvvJzo62mXbVy9JzZ01fPjhh+nfvz+jRo1i27Zt5vbdu3djtVrx8/MjISHBZZvlypXj8OHDlChRwmyzXLly5vZ7773Xoa/geknqlY4cOeLQxpV8fHyclp9eTD+fZ1siIiIiInLjNMN4m3rzzTfp2LEjv/76K8nJydhsNmw2G4mJiS7rnzp1irS0NADuuusuvL29bzhmq1atqF27Nu3atXMozz2HcdGiRdx3330u9+3Zsyfjxo3DMAwAxo0bR69evW64D7lOnTrF5MmT6dy5819uQ0RERERE/jeaYbyNJCcnExkZSXZ2No0bNyYiIoKsrCxze4UKFdi1axcZGRlOs2tnzpyhb9++GIZBVlYWcXFxecaJj49nwoTL5/vFxcXh6+sLgJeXF9OnT7+uvu7Zs8c8z7FOnTq888477Ny5k4iICLKzs7FarfTr1y/fNtq3b4+npycAX3/9tdm3iRMnkp2dzciRI6lZs+Z19UdERERERG4+i5E7JSRyh/u9AJaklgooTHp6ultjBgQEuD1mQcXVsf494wYEBAD8I8a4II8168RJt8b0CioJ6HW91XEL6lito993a1z76Kf0ut7imFAwxyp3Hs0w/o0lJCQwd+5c83ndunWZNGlSAfZIRERERETuJEoY/8aGDh3K0KFDC7obIiIiIiJyh9JFb0RERERERMQlJYwiIiIiIiLikhJGERERERERcUkJo4iIiIiIiLik22qIiIiIiIiIS7pKqvxtFNg97JaudG/M+1txbv0mt8YE8G/amL3hUW6NWWXNYi7+stutMQF8q4Vy6dhxt8b0LlOa3yf8y60xAUoNf5rfx73n3pgvDAHgwk873BrXr1YNTk37yq0xi8f2AuD3d9x7D7tSw57i5L8/dmvMkoMfA+Dijl1ujetbo6rbvxP9mzYGCuZYC+J3Dvyz7k1YEN9NWcdPuDWmV+kgAE59PtOtcYv37u7WeHJzaEmqiIiIiIiIuKSEUURERERERFxSwigiIiIiIiIuKWEUERERERERl5QwioiIiIiIiEtKGEVERERERMQlJYwiIiIiIiLikhJGERERERERcUkJo4iIiIiIiLikhFFERERERERcUsJ4m0pJSaFr164OZXv27MHT05PDhw+bZVarlSeffNJ8HhYWBsC0adOoUqUKkZGRtGzZkk8++cShrfj4eKKiohziBQUFYbVaCQsL4/vvv3dox2q1YrVaSUpKMvdp1qwZb775Zr7HYRgG5cuX56uvvjKPoWPHjg51IiIiOHLkCKdPnyY2Nhar1UqLFi0YOHAghmFcc6xEREREROTWUMJ4B5k1axYDBw50SNoAtmzZwvHjx53qDx06lBUrVmCz2UhMTGTRokXmtk2bNuHn50daWppZFhERgd1u59133+Wtt95yaMdut2O3280k9tChQwQHB7N8+fJ8+7x27Vo6dOjA3LlzAbjvvvv4/fffOXPmDADHjh0DoFy5cjz99NN069YNu93O6tWreeSRR/JMGDMyMjh79qzDIyMjI9++iIiIiIjIjVHCeAdZtWoV48ePZ/HixQ7lTz31FAkJCXnuV7hwYUaMGGEmmlu3bqV+/fp069bNTOSudPr06WvO7CUlJfHII49QqVIl9u3bl2e9xMREhg4dSkZGBunp6QB06tSJb7/9FoDZs2fTpUsXsrOz2bdvHx06dDD3bdGiBR4ert+iY8eOJTAw0OHxzjvv5NtnERERERG5MUoY7xB79uwhNDQUf39/Spcu7bAstXPnzixfvtxMyFwpV64cR44cAS4ncd27d6dTp0589913Zp3k5GSaNGlCnz59eOWVV8zyhIQEc0nqmjVrAFi+fDlt27alZ8+eJCYmuoxpGAa7d++mRo0adO7cmQULFgAQExPD7NmzAZgzZw5dunTh5MmTlCxZ0tzXarVSs2ZN9uzZ47Lt+Ph4zpw54/AYNmxYvmMoIiIiIiI3xqugOyDXJzExkU2bNtGuXTtOnjzJ7NmzGTJkCAAeHh48+uijTJkyJc/9jxw5Qrly5QBYuHAhP/zwAwA7d+40l6VGRESQlJREQkICGzZsoHbt2sDlJalPPfWU2VZqairbt28nOjqanJwczp07R3x8vFPMdevW8euvv9KuXTsyMzMpWbIkPXv25L777uPo0aPs37+frKws7r77brKzszlx4oS5r91uJzY2lszMTJfH4+Pjg4+Pj0NZfgmziIiIiEhe9oZHXbvSdaqyZvG1K91BNMN4h1i2bBnr16/HZrOxdu1ah5lBgD59+jBz5kwuXbrktO+FCxcYP348Xbt2Zdu2bURERGCz2bDZbIwZM4Z58+Y51B80aBBTpkxx2RZgJpU2m40lS5ZQtWpVl8tSExMTmTVrFjabjRUrVnD+/Hn+/PNPADp27Mhjjz3Gww8/DICnpydVqlRh4cKF5v5ZWVk3NkgiIiIiInJTKWG8ja1evZo2bdrQunVrNm3aZJ7P5+Pjg8VicViW6uPjQ0xMjMNFbBISEoiMjCQqKoqHH36Y9u3bk5iYSKtWrcw6rVu3dlpS6u3tTVRUlLls9MolqdOmTWP27NlERETk24ZhGKxYsYI6deqYZc2aNTPPXYyJiWHlypV06dLF3P7ee+8xc+ZMIiIiiIqKokiRIoSEhPzV4RMRERERuT4Wj5v3+JvRktTbVEhIiMsrn+ay2WzA5aWbuUaMGMGIESMAiI2NJTY21mm/MWPGODwvV66c2daVV18dO3as+fPV7Vz9vFevXk5xLBYL27dvdyh78cUXzZ9DQ0PJyclx2F6sWDGmT5/u1JaIiIiIiBQMJYxyUyQnJzNq1CiHsiuTWRERERGR25bFUtA9uG0pYZSbIvcejiIiIiIidxqLhxLGvPz9FtmKiIiIiIjITaEZRhERERER+Wf7G16s5mZRwigiIiIiIv9sOocxT0oYRURERETkn03nMObJYhiGUdCdEBERERERKSj72nS6aW1VXjb/prV1O9AMo4iIiIiI/KNZtCQ1T0oY5W/jUupht8f0Ln83f3yV6NaYxXrFsDc8yq0xAaqsWcy59ZvcGtO/aWPOrd3o1pgA/s2bcOnwUbfG9L67LMfffNetMQFKv/gcx157260xy7wyAoDUISPcGrf8e2/z+4R/uTVmqeFPA+7/fvIufzfpS1a4NWZA20gATvzrQ7fGDXp6IHtbtHdrzCqrFwEFc6ynZ85xa8y7uj8MQHp6ulvjBgQEcOr8RbfGLF7YFyiY76aMvb+6NaZPlUoAHI1/1a1xy44dde1KBcVDF73Ji0ZGREREREREXNIMo4iIiIiI/LNpSWqelDCKiIiIiMg/mxLGPClhFJH/Z+/Ow6oq1/+PvzegkIqooZRK2lc9zmapCSiwnXDWPKaoaWF61CzDb9oJCn9ZagszBckAACAASURBVKSmqZmW6ckujzkAmWVK4LBxwqlCm45DJ3MeqETUGMT9+8Mv67hlb4XTZqPweV3Xui7Ws5713M9apnn73GstERERkTLNpGcYHdKdEREREREREbu0wigiIiIiImWbVhgdUsIoIiIiIiJlm55hdEiptIiIiIiIiNilFUYRERERESnTTFphdEgrjMXs6NGjVK9eHbPZjNls5rXXXmPixIk2fbKysnjmmWcwm80EBgYSFxdnHPv222/p0qULoaGh9OzZk+PHjwOwdOlSGjRogNlspkuXLly4cAGAFStWEBAQQHBwML169TL6VqxYkcuXLwOwZ88eTCYT3333nREnKCiIN954w9ifN28eb7/9NgD//ve/6dSpk93rmzx5Mg8++KCxv3r1akwmE5cuXQIgLi6O4OBggoODGTNmDFlZWQBERETQpk0bOnXqRFhYGNu3b7d7v2JiYop6y0VEREREisbN5LytlFHC6AKhoaFYLBYsFguhoaEFjk+ZMoUWLVpgsVhISkpi+vTpHDx4kNzcXEaOHMmSJUtISUnhpZde4oknnjDOi4yMxGKx0KFDB5YvXw7A9OnT2bp1K9u2bWPZsmVG3yZNmrBhwwYA4uPjadOmjXHs+PHj1KlTh02bNhltY8eOJS4ujnPnzvHiiy8yc+ZMh9fn6+vLvn37APjss8946KGHADh48CALFy5k48aNbNu2jVq1ajF16lTjvA8//JBNmzbxwQcf8Le//Y2zZ88WuF839r9RdnY2Fy9etNmys7MdzlFERERERIpOCeMdICkpidGjRwPg7e3NmDFjWL16NampqQQFBfHAAw8AEBISwrVr14xVxnwXLlzAarUC11crd+/eTV5eHlWrVjX69O3bl88++wyAH374gSZNmhjH4uPjGTp0KPXq1ePIkSMAeHh4MHnyZPr06UO1atV45JFHHM7/8ccfJyEhgT/++IOcnByqVKkCwKpVq4iMjMTT0xOAF1980Wb1NF+dOnUYMGAASUlJhb5nsbGx+Pj42GzT351f6PNFRERERAwmN+dtpUzpu6I7UEpKilFimZGRUeC4yWTC7YZX+fr7+3Pq1ClOnz5N7dq1bfrmHwOYO3cuTZs2JTk5mWHDhgGwfPly5syZQ/369ZkyZYpxXpUqVfjjjz9ITU3l4Ycfthlz06ZNhIWFMXjwYJuELiAggO+//55Bgwbd8vqaNm3K999/z4YNG+jatavRfvP8vby8yM3NtTtGzZo1jeu68X7Nn28/CYyOjiYjI8Nme+nZ5245TxERERERu1SS6pASRhe4scTSx8fHbp9r164ZP584cYKaNWtSs2ZNTp48adMv/xhcL0ndv38/jRs35sSJEwC0atWKhIQEDh48yNatWzl48KBxblhYGGPHjqV///424x04cIDevXsTGxvLunXrjGOvv/46r7zyCrGxsbe9xubNmxMbG0vfvn2Ntpvnn5WVRfny5e2ef+rUKeO6brxfzz1nPwn09PSkcuXKNlv+SqaIiIiISFGYTCanbaWNEsY7QFhYGIsXLwbg0qVLvP/++wwcOJCAgAB27txpJIPbt2/HZDLh7+9vnOvh4UFUVBSvv/46AIcPHwagfPnyVKlSxSYR7devH23atKFly5ZGW3x8PHPnziUxMZGkpCQaNWrEkSNHOHToEAcOHCAqKoqWLVuyevXqW17D0KFDCQsLw9fX12gbOHAg77zzjvFs4ezZsxkwYECBc48fP05CQgJhYWFFum8iIiIiIlK89FmNEhAXF0daWhoATz75JDExMYwfPx6z2UxOTg4TJ06kYcOGAHzwwQcMHz6cnJwcKlWqxD//+c8C47Vo0YJTp05x4sQJJk6cyPnz53FzcyM4OJjGjRuze/du4PrLaRYtWmRzbkJCAmvXrjX2O3XqRFxcHDt37mTGjBkATJo0ic6dO9O7d2/uueceu9fUuHFjpk2bZtPWsGFD/va3v9GlSxejz9y5c43jw4cPp3Llyri7u/Pee+/h5+fH0aNHjZJUgJYtWzJnzpxC31sRERERkSIrhc8eOosSxmJWt25d4uPjjX2z2cwvv/xSoN97771n9/zmzZuTnJxcoD0iIsJmf9u2bQA2yZ+jvnD9Uxs3npdvyJAhBfr6+Piwd+9eu/ObPHlygTaLxWL8PHDgQAYOHOgw/s3q1q3L+fPn7R4TERERESkWpfDZQ2dRwiiFNnfuXNasWWPsa/VPRERERKR0U8IohRYZGUlkZGRJT0NERERExKlMbipJdUQJo4iIiIiIlG2l8O2mzqJUWkREREREyjaTyXlbES1YsIAHH3wQLy8vWrVqVeAdIzebM2cODRs25J577sHf35///d//JSsr67+98ttSwigiIiIiIlICVq1axfjx43nllVf45ptvCA4Opnv37hw7dsxu/+XLlxMVFcWrr77Kjz/+yJIlS1i1ahXR0dHFNkcljCIiIiIiUra5uTlvK4LZs2czYsQIRo4cSePGjZkzZw7+/v4sXLjQbv/U1FTatWvHkCFDqFu3LmFhYQwePJh9+/Y54y7YZbJardZiG11EREREROQOd+ypZ5w2lt+iOWRnZ9u0eXp64unpadOWk5NDhQoViIuLo1+/fkZ7ZGQkaWlppKSkFBh75cqVjBkzhqSkJB599FH+/e9/07NnT5566imioqKcdg030gqjiIiIiIiIk8TGxuLj42OzxcbGFuiXnp5OXl4efn5+Nu1+fn6cOXPG7tiDBg1iypQptG/fnnLlylGvXj06dOhQbMki6C2pUopkZma6PKa3tzcX133p0piVe3Ul59gJl8YEKP9Abc699Y5LY9aYOI6s7//l0pgAXk0bkXvK/h/UxaVczfs4PWmaS2MC3D/lFZfHvX/KKwBcPZ/u0rge1X05N921346t8dJ4AA637+rSuA22f8npmKkujXn/1BgAcn457tK45ev4u/zPxPIP1AZK5lozPk90aUyf3t0A1/8/1tvbu0RiQsn82VQSMQF+/zjOpXGrDhng0nhF4ua8t6RGR0fzwgsv2LTdvLp4I9NNL8qxWq0F2vJZLBamTZvGggULaNu2LUeOHCEyMpL777+fSZMm/fnJ26GEUUREREREyjaT8wov7ZWf2uPr64u7u3uB1cRz584VWHXMN2nSJIYNG8bIkSMBaN68OZcvX2bUqFG88soruBXD9yRVkioiIiIiIuJi5cuXp1WrViQnJ9u0JycnExQUZPecK1euFEgK3d3dsVqtFNerabTCKCIiIiIiZdt/8f1EZ3jhhRcYNmwYrVu3JjAwkEWLFnHs2DHGjBkDwJNPPkmtWrWMZyB79+7N7Nmzefjhh42S1EmTJtGnTx/c3d2LZY5KGEVEREREpEwzOfEZxqIIDw/n119/5fXXX+f06dM0a9aM9evXU6dOHQCOHTtms6IYExODyWQiJiaGkydPUr16dXr37s20acX3PgIljCIiIiIiUraV0AojwNixYxk7dqzdYxaLxWbfw8ODV199lVdffdUFM7tOzzCKiIiIiIiIXVphFBERERGRsq0Y3i5aWihhFBERERGRMs2khNEh3RkRERERERGxSwnjHeDo0aNUr14ds9mM2WwmJiaGunXrMn36dACysrIwm8189tlnmM1mWrZsib+/P2azmejoaCZPnkzz5s0JDQ2lf//+5ObmGmO///77NGzY0CaeyWRi1apVAPzrX/8iIiLCiDNu3DjMZjMBAQHExMQAYDabCQ4ONuZ36dIlNm/eTGBgICEhIbRr147s7GyH1xcUFMQbb7wBQGZmJq1bt7Y5PmzYMHbt2kVWVhbjx4/HbDYTEhLCkCFDuHjx4p+7uSIiIiIit2MyOW8rZZQw3iFCQ0OxWCxYLBamTp1KlSpVSEhIsEnE+vTpg8ViYc6cOYSHh2OxWIxvssTGxpKSksK9995LUlKScc66desIDAxk//79RtuDDz7I/PnzC8xhypQpNGvWDIvFwq5du+jQoYNxbMOGDcb8KlWqRExMDOvXr2fr1q2sX7+ecuXK2b2u48ePU6dOHTZt2gSAt7c3tWvX5scffwQgJyeHtLQ02rZty9SpU2nYsCEWi4WtW7cSHR3N1atX7Y6bnZ3NxYsXbbZbJa0iIiIiIg4pYXRICeMdysPDg/DwcD766KMinZeRkYHVagUgPT2dSpUqMWrUKOLi4ow+1apVo2XLljaJJUBycjKjRo0y9jt16uQwTrly5bBYLGRnZ+Pj42PzfZgbxcfHM3ToUOrVq8eRI0cAGDBgAAkJCUbMLl26YDKZSE5ONj5SCtC8eXOqVatmd9zY2Fh8fHxstlmzZt3q1oiIiIiI2Ofm5rytlCl9V3SXSklJMUo+81f/Ro0axeLFi7l27dptz4+OjqZevXpcuHCBrl27AvDJJ5/Qv39/AgMD2bt3r03/CRMmFEiwrFYrJgf/KtK9e3fMZjO9evUC4B//+AeffPIJjRo14tlnn3U4x02bNhEWFsbgwYONpLV3796sW7cOuJ5QDhgwAIBr164Z8QcNGkTLli3ZvHmzw+vNyMiw2SZMmHDb+yQiIiIiIoWnt6TeIUJDQ4mPjzf2ly5dire3N126dDFW424lNjaWzp0789hjj/H7779To0YN1q5dS3Z2NosXL+bw4cMcOHCAFi1aAFC3bl18fX1tEkk3NzeHSeOGDRuoVKmSsV+vXj2WLVvGtWvXGDFiBMnJyUaimu/EiRMcOHCA3r17c+3aNS5fvkx0dDSVK1fGz8+Pf/3rX3z99dcEBAQUiL9y5UomT57MlStX7F6vp6cnnp6eNm2ZmZm3vU8iIiIiIjdztGgiWmG840VGRjJv3rxC9fXy8mLs2LG89dZbpKen4+XlxcaNG0lMTOSjjz6yKUsF+Pvf/86MGTOM/S5durBo0SJjf8uWLQ5jHT58GLie5Pn6+tpdYYyPj2fu3LkkJiaSlJREo0aNbMpSn332WTp27Gj8Bu3SpQsLFy40znf0/KKIiIiIiFPpGUaHlDDeIW4sSR0/frzRXqNGjQJvFb2Vnj17kpiYyPLlywkODjbaAwICWL9+vU3fhx56iNq1axv7MTExfPfdd5jNZoKCgmzKQfNLUs1mM2fOnGHWrFkEBAQQGhpKeno6YWFhBeaSkJBAaGiosd+pUycjae3Tpw87duwwylHz4x88eJCQkBC6du3Kzz//XKRrFxERERER51JJ6h2gbt26nD9/3uHxG1fdACNxyzd58mTjZ3d3dw4cOFBgjHLlyvHVV18BsG/fPqN9w4YNxs9eXl688847Bc61WCwF2t577z2H8823bds2m/0hQ4YYP1euXJmsrCyb415eXsydO/e244qIiIiIOJVb6VsZdBYljOIUBw8eZPTo0TZtK1eu5L777iuhGYmIiIiIFJJJhZeOKGEUp8j/fqKIiIiIiJQeShhFRERERKRMM6kk1SEljCIiIiIiUra5qSTVESWMIiIiIiJStpXCz2E4i1JpERERERERsUsrjCIiIiIiUqaZtMLokMlqtVpLehIiIiIiIiIl5fQrU5w21v3TJjltrDuBVhil1Lh6Pt3lMT2q+3LJst2lMSuZ23N5+y6XxgSo2D6ArB8PujSmV+OGHG7f1aUxARps/5Ir+75xacwKrR8mM2mzS2MCeId15GLiJpfGrNytEwBZ3//LpXG9mjYquWstgd87l7btdGnMSsFBAGQf/smlcT0b1HP5n4kV2wcAJXOtJfH/HIDMzEyXxvX29i6RmFAyfzaV1LVeStnh0riVQtu5NJ44hxJGEREREREp21SS6pASRhERERERKduUMDqkhFFERERERMo0k77D6JDujIiIiIiIiNilFUYRERERESnbVJLqkBJGEREREREp29yUMDqiklQRERERERGxSyuMIiIiIiJStqkk1SEljCIiIiIiUqbpLamO6c6IiIiIiIiIXUoY/0tHjx7FZDKxe/duABITE5k8eTK///47gwcPJjQ0lPbt22OxWG7ZH8BsNnPp0iWbsR9//HGSkpJ49tlnbeI2b96c7OxsWrduDcDSpUvx9fXlypUrAERFRRkxf/jhB3r06IHZbKZ9+/YsWLDAGOfQoUO4u7tz8uRJo81sNhMcHEzbtm154403jHZvb2/MZjNms5mRI0ca7e+//z4NGza87b2Kjo6ma9euxn5wcDBnz5419pcsWcKbb74JwKJFiwgJCSE0NJRu3brx7bff3nZ8EREREZE/xeTmvK2UKX1X5EJNmjRhxowZNm3jxo3jySefJCUlhfj4eJ599ll+++03h/1vpWPHjmzdupVr164BsHv3blq2bImnp6dNv/vuu4/FixfbtOXk5DBixAgWLFiAxWJh27ZtNGvWzDi+evVqRo8eTXx8vM15GzZsYNeuXSxbtsxIYhs2bIjFYsFisdjEWbduHYGBgezfv/+W17Fnzx7uuecefv31VwD69+/PJ598YhxPSEjg8ccfZ+PGjaSkpLB582ZSUlL45z//ibu7u90xs7OzuXjxos2WnZ19y3mIiIiIiNjlZnLeVsooYfwTGjduzNWrV/nXv/4FQF5eHocOHaJ79+7A9USuX79+fPHFF3b7346HhwcBAQHs3LkTgPj4eAYMGFCgX0REBMuWLePq1atG265duwgKCqJu3boAmEwmQkJCjONbt25l5syZfPnllwXGy87OJjs7m7y8PIdzS09Pp1KlSowaNYq4uDiH/b755hseeeQRBg4cyJo1awB4/PHHjYTxwoUL/Pbbb9SvX5+PP/6YqKgoPDyuP1rr6+tLkyZN7I4bGxuLj4+PzTZ97lyH8xARERERccRkMjltK22UMP5JL774IjNnzgSuJ1F+fn42x/39/Tl16pTd/oUxYMAAYxUwOTnZprQzn5eXF3379mXFihVG2+nTp6lZsyYAaWlpmM1mHn30UeB6OWrDhg2pWLEifn5+NmWp3bt3p1atWkRERODj4wPAwYMHjZLUmJgYAD755BP69+9PYGAge/fudTj/uLg4wsPD6du3r5E4165dm+zsbNLT01m7di19+/YtMOc333yTgIAAm9LYG0VHR5ORkWGzvRQZWYg7KiIiIiIihaWE8U9q3749P//8MydPnsTX15dz587ZHD9x4oSRBN3cvzA6duxISkoK+/bto1mzZgXKUfM9++yzLFiwAKvVCkDNmjWNGC1btsRisRilrXFxcezZs8d4RjAhIcEYZ8OGDXzzzTekpqYabTeWpE6dOhWAtWvX8t5779G9e3cOHz7MgQMH7M5r/fr1xMTE0L9/f7766iujLLVfv36sXbvWZtX0xjlHRUXx5ptvGuW8N/P09KRy5co2m6N7IyIiIiJyS3qG0aHSd0UlYPz48cyZMwd3d3caNGhAUlISAGfPnmXNmjX07NnTbv/C8PDwoFWrVrz00kt2y1HzVa1alcDAQNavXw9A27ZtSU1N5ejRowBYrVajxHTjxo2kpqaSmJjIjh07jJW/fA888ACNGzc2xrpZeno6Xl5ebNy4kcTERD766CO7ZalpaWmEhoaSmJhIYmIi06ZN49NPPwWul6UuXbqU8+fPU79+fQCGDBnC9OnTjdLaG0tsRURERESKjZ5hdEgJoxP07t3bSMbeeecdPvzwQ8xmM/3792f+/PlUq1bNYf8b2zp37kznzp0LjD9gwAD27t1rtxz1Rv/7v//LwYMHAShfvjxLlizhmWeeoUOHDnTp0oVhw4Zx+PBhfHx8cPu/b814enpiMpkKrHiOGjWKd955B7AtSR00aBBr1qwhODjY6BsQEGA3uYyLi6NDhw7GfqdOnYzE0t/fn6tXr9KnTx+b4yEhIXTo0IHOnTvz9ttvEx4efstrFhERERGR4mOy5tcwitzlrp5Pd3lMj+q+XLJsd2nMSub2XN6+y6UxASq2DyDrx4MujenVuCGH29/6H0qKQ4PtX3Jl3zcujVmh9cNkJm12aUwA77COXEzc5NKYlbt1AiDr+8K9AMxZvJo2KrlrLYHfO5e27XRpzErBQQBkH/7JpXE9G9Rz+Z+JFdsHACVzrSXx/xyAzMxMl8b19vYukZhQMn82ldS1XkrZ4dK4lULbuTReUZybNd9pY9WY8JzTxroTeJT0BKT06N69O3/88YexHxUVRbdu3UpwRiIiIiIit2cqhaWkzqKEUZxmw4YNJT0FEREREZGiK4Uvq3EW3RkRERERERGxSyuMIiIiIiJStplUkuqIEkYRERERESnb9AyjQypJFREREREREbv0WQ0RERERESnT0t/9wGlj+T77N6eNdSdQSaqUGq7+hhFc/47Rb1eyXBqzWgUvl8fMj1sWvsNVUnHL2rVCyXzXrSR+v0LZ+oadfl2LT1m7Vld/h7fB9i+BsvX7taT+e7oj6S2pDilhFBERERGRsk3PMDqkVFpERERERETs0gqjiIiIiIiUaSZ9VsMhJYwiIiIiIlK2KWF0SCWpIiIiIiIiYpdWGEVEREREpGxz0zqaI0oYRURERESkbFNJqkNKpUVERERERMQurTCKiIiIiEiZprekOqaEUUREREREyjY9w+jQXX9nLBYLoaGhhISEMGjQIH7//XcmT55M8+bNMZvNhIaGcurUKQDefvttAgICaNeuHaNGjbI73kcffURwcDABAQG8+OKLRvvEiRMJDg7miSeeICcnx+65Fy5c4Mknn8RsNtO2bVveffddAOrWrcv06dMByMrKwmw289lnn2E2m2nZsiX+/v6YzWaio6MLjHngwAFCQkIIDQ0lICCA48ePO7xugGvXrvHaa68RGhpKUFAQTz/9NAARERF89913xrhBQUG88cYbxv7SpUuZP3/+Le91VlYW48aNw2w2ExAQQExMDABms5lLly5hsViMawkODubo0aNFmhOAt7c3ZrMZs9nMyJEjbzkfERERERGnMJmct5Uyd/UK42+//cZLL71EYmIiVatWZdmyZYwbN4769esTGxtLr169WLZsGR9++CHPP/88q1atIjU1FZPJZCRYNxsyZAhPPfUUAB07duT48eOkp6dz5swZtm3bxrRp04iPj2fIkCEFzn3uuecYOnQo3bp1w2q1smXLFgCqVKlCQkIC48ePN/r26dOHPn36YLFYWLduHW+99Zbd+UyZMoWFCxfStGlT/vjjD0wmk8Pr/uc//8mSJUvIysoiJSUFgE2bNhUY8/jx49SpU4dNmzbx8ssvF/p+T5kyhWbNmvHOO+84HDs8PJy33nqLjz/+mHfffZeZM2cWak75GjZsiMViue1csrOzyc7OtmnLycnB09Oz0NcjIiIiIiK3dlevMK5bt44hQ4ZQtWpVAIYNG0Zqaip5eXlGn8zMTLy9vXFzc+PXX38lLS0Nq9VqnHOzcuXKAXD16lV8fHy49957SU1NJSwsDIBu3bqxc+fOAufl5eVx5MgRunXrBlyvg+7YsSMAHh4ehIeH89FHHxX5GitUqMCWLVu4fPky99xzD15eXre87pUrV9qsVHbq1KnAmPHx8QwdOpR69epx5MiRQs8lOTnZZmXW3tj5Lly4gNVqBSjUnIoqNjYWHx8fm23WrFl/elwRERERKYPc3Jy3lTJ39RWdPn2a2rVr27TVqFGD9PR0oqOjjbLLAQMGULFiRRYuXMgrr7xC/fr1Wbx4scNxZ86cyV/+8hfuvfdeKlSowIULF6hcuTIAPj4+/PbbbwXOSU9Pp3r16g7HHDVqFIsXL+batWtFusaZM2fy/fff89BDDzFo0CCuXLlyy+u+ePGiMVdHNm3aRFhYGIMHDyYuLq7Qc7Farbd9IHjVqlU8/PDDzJw5k8jISIBCzSnfwYMHjZLU/JJXe6Kjo8nIyLDZJkyYUOhrERERERHJZ3IzOW0rbe7qhLFmzZqcPHnSpu38+fP4+voSGxvLzp07ee+994zEo3Pnzqxfv560tDTeffddLl26ZHfcF198kcOHD3Pu3Dl27dpF1apVuXjxInB95axatWoFzvH19eX8+fMO5+rt7U2XLl1ISEgo0jXWqFGDhQsXcuTIERo2bMiyZctued0+Pj7GXO05ceIEBw4coHfv3sTGxrJu3bpCz8XNzc1YNXQkPDycr7/+mr/+9a/88MMPALed043yS1ItFgtTp0512M/T05PKlSvbbCpHFREREZG7zYIFC3jwwQfx8vKiVatWbNu2rVDnrVy5EpPJxGOPPVas87urE8aePXuyYsUKLly4AMDHH39MYGAg7u7uRp8qVarw22+/kZWVZbwwplKlSnh5edkdM/+5OHd3dypWrEiFChUICAggKSkJgC+//JJ27doVOM/d3Z0GDRqQmJhotN38LF5kZCTz5s0r0jUePnzY+LlGjRpcu3btltc9aNAgYmNjjXPyn6PMFx8fz9y5c0lMTCQpKYlGjRoVuiy1S5cuLFq0yOHY+UwmE1FRUUbCd7s5iYiIiIiUqBJ66c2qVasYP348r7zyCt988w3BwcF0796dY8eO3fK8X375xXgpZ3G7qxPGatWqERsbS9++fQkJCWHt2rVGQhYdHY3ZbGbixIm8/PLL5ObmEhERQfv27QkKCmLIkCFUqlSpwJgzZszAbDbTvn17GjZsSIsWLXj44Ye57777CA4O5ocffqB///525zNv3jyWL1+O2WwmMDCQH3/80eZ4jRo1aN26dZGuceXKlTz66KOYzWaSk5MZNmzYLa/76aefxsvLi5CQENq3b8/y5cttxktISCA0NNTY79Spk1GWOn/+fDp37kznzp2NBPlGMTExfPfdd5jNZoKCgti8ebPDeVevXp3atWuza9euW87pb3/7mxHz7NmzNiWpgwYNKtK9EhERERH5r5jcnLcVwezZsxkxYgQjR46kcePGzJkzB39/fxYuXOjwnLy8PJ544glee+01/ud//ufPXvltmay3qzEUuUtkZma6PKa3tze/XclyacxqFbxcHjM/rqvvsbe3d4n9uupaizcmuP73bEn9foWSVV9e2gAAIABJREFUuVb9uhafkvx1LUvXerh9V5fGbLD9S6Bs/X4tqf+e7kQXVn3itLHueaxngbf5e3p6Fnh8KicnhwoVKhAXF0e/fv2M9sjISNLS0owvDNzs1Vdf5cCBA6xZs4aIiAguXLjAp59+6rT53+yu/qzGn7Vq1Sqb7P2+++5j5cqVhTp3+PDh/Pzzz8Z+REQEERERJTYfZ4uOjiY1NdXY79atG1FRUSUyFxERERGRu0VsbCyvvfaaTdurr77K5MmTbdrS09PJy8vDz8/Ppt3Pz48zZ87YHXvHjh0sWbKEtLQ0p875Vsp0whgeHk54ePh/de6HH37o5Nn8ufk4243PHIqIiIiIlGpFfPbwVqKjo3nhhRds2m71csabv0Lg6MsEmZmZDB06lA8++ABfX1/nTLYQynTCKCIiIiIiUtRnD2/FXvmpPb6+vri7uxdYTTx37lyBVUeAn376iaNHj9K7d2+jLf+TfR4eHhw8eJB69er9ydkXdFe/9EZERERERORuVL58eVq1akVycrJNe3JyMkFBQQX6N2rUiG+//Za0tDRj69OnDx06dCAtLQ1/f/9imadWGEVEREREpGxzc15JalG88MILDBs2jNatWxMYGMiiRYs4duwYY8aMAeDJJ5+kVq1axMbG4uXlRbNmzWzOr1KlCkCBdmdSwigiIiIiImWavWcGXSE8PJxff/2V119/ndOnT9OsWTPWr19PnTp1ADh27BhubiVbFKrPaoiIiIiISJmW8cnnThvL56+9b9/pLqIVRhERERERKdtKqCT1bqCEUUqNkvrouTM/9FoYVcL/SuYm+x9yLU7enUJJX7DEpTF9x47g8vZdLo0JULF9ALlnzro0Zrn7/Dgd/drtOzrZ/bGvcuqlV10as+b069f5x9f7XRr3nkce4uwbs10a0+/l669VPz1pmkvj3j/lFQ4Hd3dpzAbbNgBwZc9XLo1b4dFWXNq81aUxK3UMAeDyrn0ujVsxoDW/r0hwacyqg/sDJfOB95L6mP2Vfd+4NG6F1g+Te+KkS2OWq10LgPPvvO/SuNXHjXZpvCIp4bLPO5kSRhERERERKduc+FmN0kZ3RkREREREROzSCqOIiIiIiJRpJfWW1LuBEkYRERERESnb9NIbh1SSKiIiIiIiInZphVFERERERMo2laQ6pIRRRERERETKNr0l1SEljCIiIiIiUqaZ9AyjQ0qlRURERERExC4ljLdx9OhRqlevjtlspnXr1qxcuRKAixcvcs8997Bz506j74oVKwgICCA4OJhevXoBsHTpUho0aIDZbMZsNhMfH8/Ro0cxmUxs2bIFgJycHKpWrcr8+fMBbPrnjxMREUH37t2NWAEBARw7dszo5+3tjdlsNvpYrVZq167Nxx9/bJwzefJkmjdvTmhoKP379yc3NxcAs9lMcHCwMdalS5cAOHToEO7u7pw8edLh/YmIiMBsNhv7M2bMoHr16sb+vHnzCAkJoV27dsTExHDt2jUjZkhICJ06daJ379589913AFgsFvz9/Y255N8TEREREZFiYzI5bytlVJJaCKGhocTHx3PlyhXat2/PoEGD+Oyzzxg+fDhxcXEEBQUBMH36dPbs2UP58uX5/fffjfMjIyN57rnnjP2jR4/SunVrPvnkEzp06MDGjRtp0KCBcdzHxweLxVJgHidOnGD//v089NBDADzwwANGv9atW9ucs2PHDnr06MGaNWsYMmSI0R4bG0uvXr0YNWoUSUlJ9OzZE4ANGzZQqVIlm3irV69m9OjRxMfHExkZ6fD+ZGVlcfbsWfz8/NixYwf+/v4AJCcnk5qaisViwc3NjZEjR/KPf/yDkSNHArB+/XoqVarEgQMHGDhwIF9//TUA4eHhvPXWWw7jAWRnZ5OdnW3TlpOTg6en5y3PExEREREpwE3raI7ozhTBlStXqFChAgBr165l2rRpHDhwAKvVClxPnHbv3k1eXh5Vq1a95Vh16tTh2LFjWK1W1qxZQ79+/W4b/8UXX2TGjBmFmmtcXByRkZFkZ2eTmZlZ4HhGRoYxb0e2bt3KzJkz+fLLL2/Zr1+/fnz66accO3aM2rVr4/Z/v+FWrFhBVFSUsR8VFcWKFSsKnN+iRQtat27N3r17C3VtcD3x9fHxsdlmzZpV6PNFREREROT2lDAWQkpKCmazmaZNmzJ8+HAyMzOxWq1UrVqV9u3bs2vXLgCWL1/OnDlzqF+/PlOmTDHOnzt3rlFiuX37dqM9MDCQrVu3cv78ee6//36jPSMjw+ifvxoH8Mgjj/Drr7/y888/33K+VquVgwcP0rRpU/r168e6deuMY9HR0dSrV48LFy7QtWtXo7179+42JbCHDh2iYcOGVKxYET8/v1uWpXbp0oWNGzcSHx/PX//6V6P99OnT1K5d29j39/fn1KlTdseoWbOmcWzVqlU2Jbz2REdHk5GRYbNNmDDhlvdFRERERMQek8nktK20KVJJal5eHvPnz+fw4cPMnz+fn376iV9++YWOHTsW1/zuCDeWpLZp0wYvLy8OHTpEt27duHz5MpmZmQQGBtKqVSsSEhLIycmhZ8+eHDx4ELBfkgrQv39/wsPDefLJJ23iOSpJBZgwYcJtyzV37tzJTz/9RLdu3cjJycHX15fBgwcD11fmOnfuzGOPPcbvv/9OjRo1gIIlqXFxcezZs4du3bqRnp5OQkICzz//vN145cuXp2LFinz++ec2fWrWrMnJkye59957gesltTVr1rQ7xqlTp+jZsyd5eXmFKkn19PQsUH5qbyVVREREROS2VJLqUJHuzLhx4/juu+/YtGkTAPfeey8vvfRSsUzsTuTl5cXVq1eJj49ny5YtJCYmsm3bNr7++musViuHDx8GridQVapUMV7w4kiDBg1o3749jz/+eKHn0KVLF/bv3096errDPnFxcaxevZrExEQ2b97MlStXjBfZ5F/H2LFjb5mUbdy4kdTUVBITE9mxYwdffPHFLef19NNP07dvXzw8/vNvEIMHD2bGjBlG6euMGTNsnqfM99133/HVV1/Rpk2bW8YQERERERHXKtIK486dO0lLS+Phhx8GoEqVKuTk5BTLxO4k+SWply9fZsiQIWzYsMFYNQP4y1/+wq5du3jzzTc5f/48bm5uBAcH07hxY3bv3s3cuXON0sqb3yo6b968AvHyS1Lz3bzaOG7cOAYNGmR3rlarlc2bNzN79myjLSgoiM8//9ymX8+ePYmJiWHy5MnA9ZJUd3d3AKZMmYKPj4/x7KGnpycmk4mTJ09Sq1Ytu3FDQkIICQmxaevcuTM//PADoaGh5OXlYTabGT58uHG8R48elCtXjgoVKrBq1Sq8vLyA6yWp+/btA6Bbt25ERUXZjSkiIiIi4hSlsJTUWYqUMOb/hT5fXl7ebVfR7nZ169bl/PnzNm2vvvqqzf6iRYuA6y/CuVlERAQREREF2m9+Nu/GPvkrlTdaunSp8XN4eDjh4eE2x/MTLJPJxIEDB2yOvfzyywXGc3d3N/rZK38NDg622U9MTCzQ5+Z53TwXgOeff95uKaujkluz2czx48ftHhMRERERKRZuShgdKVLC2KJFC5YvX47VauXo0aPExsYWWFWS0is6OprU1FRjX6t/IiIiIlIamEx6htGRIiWMs2fPZsKECZw+fZq2bdvSp0+fQn/mQe5+sbGxJT0FERERERFxoUInjHl5eXz55Ze8//77vP/++8U5JxEREREREdfRM4wOFXrt1d3d3eZFKiIiIiIiIqWCm8l5WylTpGLd1q1b2zzDJiIiIiIiIqVXkZ5h3Lp1K++++y5/+ctfbD7yvmfPHqdPTERERERExCX00huHipQwzpkzp7jmISIiIiIiUiJMpbCU1FlMVqvVWtKTEBERERERKSlX9n7ttLEqtHnEaWPdCYq0wtihQwdMdt4gtHnzZqdNSOS/lXvytMtjlqt1P+fnLHRpzOrjn+H0K1NcGhPg/mmT+H35apfGrPrEQC6l7HBpTIBKoe3IPXHSpTHL1a7FL0NGujQmQJ2PF3N04HCXxqy7+kMAfn3/Q5fGvXf0cI49/ZxLYz7wj/kA/L4iwaVxqw7uXyJ/NgFcXJ/k0riVe4S5/M/E+6dNAkrmWs/Pfc+lMatHjgEgMzPTpXG9vb1LJCaUzJ9Nrv47TLla9wNwOKSHS+M22LrepfHEOYqUME6cONH4OSsri48//pj69es7fVIiIiIiIiIuo89qOFSkhLFnz542+3379qVHD9f+y4SIiIiIiIgzmdz00htH/tSduXbtGj///LOz5iIiIiIiIiJ3kCKtMA4YMMB4hjEvL4/9+/cTFhZWLBMTERERERFxCa0wOlSkhLFXr17/OdHDgxdffJGAgACnT0pERERERMRl9AyjQ0VKGP38/OjWrZtNW2JiYoE2ERERERERufsVae315ZdfLlSbiIiIiIjIXcPN5LytlCnUCuORI0c4dOgQFy9eZP36/3w/JSMjgytXrhTb5ERERERERIqbyaRnGB0pVMK4Y8cOli5dytmzZ5k5c6bRXrlyZWbNmlVskxMRERERESl2eobRoUKl0k899RRbtmxhzpw5bNmyxdjWrl1b4NuM4tjRo0epXr06ZrMZs9lMTEwMrVu3tuljsVjw9/c3+syfP58hQ4awd+9eo8+mTZsYM2YMAFarldq1a/Pxxx8D8Nlnn2E2m2nZsqUxTnR0NNeuXSMmJobQ0FCCgoKYN2+eMV6DBg0wm820adOGpUuXAnDx4kV69+6N2Wzm0Ucf5fPPP7d7TWazmYiICGP/2WefNa7pdjE7dOhAx44dCQ8P59ixYwAsXbrUmI/ZbCY+Pv6/vNsiIiIiIvJnFemlNyNGjODs2bN8//33ZGVlGe09evRw+sRKq9DQUJskKDExsUCf8PBw3nrrLWO/du3axMfH06ZNGwDi4+MZMGAAcH31t0ePHqxZs4YhQ4bQp08f+vTpg8ViYd26dcY4H3zwAbm5uaSkpJCbm0ufPn1o0qQJnTt3xsfHB4vFQlZWFk2bNiUiIoJly5bRrVs3nn32WaxWKxkZGQ6v6dSpU+Tm5uLu7s6JEyeM9iVLltwy5pYtWwBITk5m0KBB7NixA4DIyEiee+65W97H7OxssrOzbdrcsrPx9PS85XkiIiIiIgWUwmcPnaVIxbofffQRAQEBPPbYY0yaNIk+ffrwxhtvFNfc5P9069aNpKQk4Pqq3fbt2zGbzQDExcURGRlJdnY2mZmZDsdYuXIl0dHRAJQrV44JEyawYsUKmz6XLl0iNzcXgAoVKrBnzx7Onj2LyWSiSpUqDsfu1KkTmzdvZvv27bRr165IMQG6dOmCh4eHTbJ5O7Gxsfj4+Nhs0+e/U+jzRUREREQMJpPztlKmSAnjrFmz+Prrr6lXrx5fffUVmzdvplGjRsU1t1IpJSXFptzUnlWrVtmUZHp5edG4cWPS0tLYtm0bQUFBuLu7Y7VaOXjwIE2bNqVfv36sW7fOYdzMzEybpM/f359Tp04B119eFBoayoMPPsiUKVMAGDZsGA0bNqRr164EBQVx+PBhh2P369ePNWvWkJCQQP/+/QsV82Y1a9Y0js2dO9e4/u3bt9vtHx0dTUZGhs320nPjHM5RRERERESKrkglqeXKlaNq1apcvXoVgJCQEKKiooplYqXVzSWp+c8M3ujmklSAAQMGkJCQwIULF4xy1J07d/LTTz/RrVs3cnJy8PX1ZfDgwXbjent7k5GRgY+PDwAnTpygZs2aAPj4+JCSksLXX3/N22+/zVNPPYWHhwcvv/wyL7/8Mlu2bOH//b//Z3d1EK4ne2fPniU3N5d69eoVKubNTp06Rc2aNfnxxx8LVZLq6elZoPw0N/PyLc8REREREbFHb0l1rEh3xtPTE6vVyl/+8hfeeecdPvvsM9LT04trbnKD7t27k5iYyNatW23KUVevXk1iYiKbN2/mypUrXLp0ye75gwcPZvr06QBcvXqV2bNnM2TIEJs+jzzyCFeuXOGHH37gl19+IScnB4AaNWpw7dq1W85v6NChBZLVwsSE6y/xuXr1KrVr1779jRARERERcTZ9h9GhIq0wTp06lYsXLzJjxgzGjBnDhQsXWLBgQXHNrVTKL0kFaNmyJRcvXqRz584A3HfffYwcOZJVq1axb98+4Przi1FRUXh5eVG/fn28vb3x8PDAarWyefNmZs+ebYwdFBTE559/bneV8emnn2bSpEmEhoaSm5vLoEGD6NSpU4F+o0aNYt68efTs2ZOBAwdyzz33ADgsn813YylqYWJmZGTQoUMHTCYT1atXZ+XKlZj+r+Z77ty5xipsRESEzVtYRURERESczk0rjI4UKWHs2LEjcL2EMTk5uVgmVJrVrVuX8+fP27TNmTOnQL/jx4/bPf/GklCTycSBAwdsjr/88svGz/nPAOZzc3Nj2rRpdsfNT04BunbtSteuXQHo3bu3gyv5D4vF4nC8W8V09EykEkQRERERkTtHkVLpkydP8thjj9GqVSsA0tLS7CY8UjoNHz7cSETNZrPd5y9FRERERO42JpPJaVtpU6QVxtGjRzNo0CBmzpwJQLNmzRg2bBjjx48vlsnJneXDDz8s6SmIiIiIiDifSlIdKtKdOXPmDEOHDsXt/26oh4cHHh5FyjlFRERERETkLlGkbC//ZSv5fv/999u+PVNEREREROSOVgpLSZ2lSCuMAwYMYMyYMWRmZrJ06VK6du3KiBEjimtuIiIiIiIixc9kct5WyhRphXHChAmsWLGCCxcusH79ep5//nmGDh1aXHMTERERERGRElTkBxAHDx5s9zt/IiIiIiIidyOTW+lbGXQWk/XGhxIdePTRR9mzZw8AkyZNYsqUKcU+MREREREREVfIPXnaaWOVq3W/08a6ExRqhTE3N9f4+YsvvlDCKHek3BMnXR6zXO1a/P5xnEtjVh0ygMPB3V0aE6DBtg1cTt3j0pgVAx/l8o7dLo0JULFdW5f/91Sudi3OvjHbpTEB/F5+gTOvT3dpzPv+30sAnHjuRZfGrT1/JudmzHNpzBp/fx5w/Z9P5WrXIjNps0tjeod1BOD83PdcGrd65BgOt+/q0pgNtn8JwPl33ndp3OrjRnNh9RqXxqwysB8AmZmZLo3r7e3Nb1eyXBqzWgUvAE48/5JL49aeN53swz+5NKZng3oAnIqa7NK4Nd90bbwiKYXPHjpLoV56Uxo/QCkiIiIiIiK3VqgVxvPnz7NgwYICP+cbO3as82cmIiIiIiLiCnqG0aFCJYydO3dm7969BX4GrT6KiIiIiMjdzWQq0tcGy5RCJYwffvhhcc9DRERERERE7jBF/qyGiIiIiIhIqaKSVIeUMIqIiIiISJn2h5en08bydtpIdwYV64qIiIiIiIhdShhFRERERETErkIljG3atOHRRx91uImIiIiIiEjRLViwgAcffBAvLy9atWrFtm3bbtk/ISGBJk2a4OnpSZMmTVizZk2xzq9QzzC+9dZbAKxbt45Dhw7x9NNPA7B06VIeeuih4pudiIiIiIhIKbVq1SrGjx/PggULaNeuHe+//z7du3fnhx9+4IEHHijQPzU1lfDwcKZMmUK/fv1Ys2YNAwcOZPv27bRt27ZY5lioFcbQ0FBCQ0PZt28fn376KX379qVv377Ex8eTkpJSLBMrC44ePUr16tUxm82YzWZee+01Jk6caNMnKyuLZ555BrPZTGBgIHFxccaxb7/9li5duhAaGkrPnj05fvw4cD2Rb9CgAWazmS5dunDhwgUAVqxYQUBAAMHBwfTq1cvoW7FiRS5fvgzAnj17MJlMfPfdd0acoKAg3njjDWN/3rx5vP322wD8+9//plOnTg6v8dChQ7i7u3Py5EkAFi1axMyZM43jly9f5uGHHwbghx9+oEePHpjNZtq3b8+CBQuKeEdFRERERO4es2fPZsSIEYwcOZLGjRszZ84c/P39Wbhwod3+c+bMoUuXLkRHR9OoUSOio6Pp1KkTc+bMKbY5FukZxhMnTpCVlWXsZ2dnc+LECadPqiwJDQ3FYrFgsVgIDQ0tcHzKlCm0aNECi8VCUlIS06dP5+DBg+Tm5jJy5EiWLFlCSkoKL730Ek888YRxXmRkJBaLhQ4dOrB8+XIApk+fztatW9m2bRvLli0z+jZp0oQNGzYAEB8fT5s2bYxjx48fp06dOmzatMloGzt2LHFxcZw7d44XX3zRJgG82erVqxk9ejTx8fEA/PWvf2Xt2rXG8XXr1tGrVy9ycnIYMWIECxYswGKxsG3bNpo1a+Zw3OzsbC5evGizZWdnO+wvIiIiIuIKhf17ak5ODl999RVhYWE27WFhYezcudPu2KmpqQX6d+3a1WF/ZyhSwhgeHk5gYCCxsbHExsbSrl07Bg0aVFxzEyApKYnRo0cD4O3tzZgxY1i9ejWpqakEBQUZS9UhISFcu3bNWGXMd+HCBaxWK3B9tXL37t3k5eVRtWpVo0/fvn357LPPgOurfE2aNDGOxcfHM3ToUOrVq8eRI0cA8PDwYPLkyfTp04dq1arxyCOPOJz/1q1bmTlzJl9++SUAvr6+VKhQwZhnfHw8AwYMYNeuXQQFBVG3bl0ATCYTISEhDseNjY3Fx8fHZpv+7vzb31ARERERkWJk7++psbGxBfqlp6eTl5eHn5+fTbufnx9nzpyxO/aZM2eK1N8ZipQwTp06lSlTppCens758+eNffnvpaSkGCWpGRkZBY6bTCbc3P7zy+Tv78+pU6c4ffo0tWvXtumbfwxg7ty5NG3alOTkZIYNGwbA8uXLmTNnDvXr17f5datSpQp//PEHqampRnlovk2bNhEWFsbgwYNtymEDAgL4/vvvb/kPBocOHaJhw4ZUrFgRPz8/oyz18ccfJyEhgT/++IOffvqJFi1acPr0aWrWrAlAWloaZrP5li9Uio6OJiMjw2Z76dnnHPYXEREREXEFe39PjY6OdtjfZDLZ7Fut1gJtf6b/n1Xkz2r06NGDcePGMXv2bHr27FkccypTbixJ9fHxsdvn2rVrxs8nTpygZs2a1KxZ00jAbj4G10tS9+/fT+PGjY2y4VatWpGQkMDBgwfZunUrBw8eNM4NCwtj7Nix9O/f32a8AwcO0Lt3b2JjY1m3bp1x7PXXX+eVV16x+68l+eLi4tizZw/dunXj22+/JSEhAfhPWer69evp0aMHgM31tGzZEovFYnPdN/P09KRy5co2m6en8z64KiIiIiLy3yjs31N9fX1xd3cvsDp47ty5AquI+e67774i9XeGIiWM27Zto06dOkap4N69e43VKykeYWFhLF68GIBLly7x/vvvM3DgQAICAti5c6eRDG7fvh2TyYS/v79xroeHB1FRUbz++usAHD58GIDy5ctTpUoVm4SsX79+tGnThpYtWxpt8fHxzJ07l8TERJKSkmjUqBFHjhzh0KFDHDhwgKioKFq2bMnq1avtzn3jxo2kpqaSmJjIjh07+OKLL4DrvznKlSvH/PnzGTBgAABt27YlNTWVo0ePAtf/pSQvL88Zt1BERERE5I5Tvnx5WrVqRXJysk17cnIyQUFBds8JDAws0D8pKclhf2co1Gc18v39738nJSWFxx9/HLj+fcavv/66WCZWVsXFxZGWlgbAk08+SUxMDOPHj8dsNpOTk8PEiRNp2LAhAB988AHDhw8nJyeHSpUq8c9//rPAeC1atODUqVOcOHGCiRMncv78edzc3AgODqZx48bs3r0buJ7ELVq0yObchIQEmxfUdOrUibi4OHbu3MmMGTMAmDRpEp07d6Z3797cc889Rt/Dhw/j4+NjlNN6enpiMpk4efIktWrVon///syaNcv4LEv58uVZsmQJzzzzDFlZWbi7u+sfI0RERESkVHvhhRcYNmwYrVu3JjAwkEWLFnHs2DHGjBkDXM8HatWqZVT1RUZGEhISwvTp0+nbty9r165l48aNbN++vdjmWKSE8erVq9SrV8+mrXz58k6dUFlSt25d4+2hAGazmV9++aVAv/fee8/u+c2bNy/wLwwAERERNvv5H/+8Mflz1Beuf2rjxvPyDRkypEBfHx8f9u7dW6C9QYMGfPrppzZtiYmJxs+jR482XuaT78a3tYqIiIiIlHbh4eH8+uuvvP7665w+fZpmzZqxfv166tSpA8CxY8ds3mcSFBTEypUriYmJYdKkSdSrV49Vq1YV2zcYoYgJo5eXF5cuXTIeqvz+++/x8vIqlonJ3WXu3LmsWbPG2G/ZsmWxfg9GRERERKQ0GDt2LGPHjrV7zGKxFGh7/PHHjYpPVyhSwjhp0iS6du3KqVOniIiIIDEx0W4ZpJQ9kZGRREZGlvQ0RERERESKLNe9XElP4Y5VpIQxLCyMBg0akJiYiNVqJSYmhvr16xfX3ERERERERKQEFfk7jA8++CDPPPMMY8eOpX79+kydOrW45iYiIiIiIlLsrFbnbaVNkRLGTz75pFBtIiIiIiIid4trVqvTttKmUCWpycnJJCUlcerUKf7+978b7RkZGcU2MREREREREVewlsJEz1lM1kLcnZSUFCwWC++9957xTRCAypUr069fP+rWrVuccxQRERERESk25zKvOG2sGt4VnDbWnaBQCWO+/fv3Gx9aF7nT5J464/KY5Wrex7kZ81was8bfn+fsG7NdGhPA7+UXODdrvktj1pjwHJlJm10aE8A7rCN/pH3r0pj3tGzO4eDuLo0J0GDbBv7dc6BLY/7PF6sB+G3ZKpfGrTYsnJ+6/tWlMet9ef2xjTOvxro07n2vRXMqarJLY9Z883q8i+u+dGncyr26cnbaWy6N6ffKRKBkrvXcW++4NGaNieMAyMzMdGlcb2/vEokJJfNnU87RYy6NWb7uAwD8MmSkS+PW+XixS+MVxdmLl502ll/lik4b605QpLekPvTQQ3zyySekpaWRlZVltM+YMcPpExNA9IcIAAAgAElEQVQREREREXGF0vjsobMU6aU348eP58MPP2Tx4sXk5eWxcuVKfv311+Kam4iIiIiIiJSgIiWMmzZtYu3atVSvXp1Zs2axd+9ezp07V1xzExERERERKXb6rIZjRSpJ9fLyws3NDZPJRG5uLn5+fpw8ebK45iby/9m797iqqvz/4y9UQkXEC5iSmmWEl1KsSOSHcEIFyguZ4m2iwHQss5ipScUu2jRK5WRSlk41I32zTDmk4u1UXg7ihUjTyCwvTZZ4abwCoiLK+f3hwz2dOAfBgYPB+9ljPx5nr732+qy9xZMf1lp7i4iIiIhUOz0l1blKJYxeXl6cOXOG0NBQHn74YVq3bo27u3t19U1ERERERKTalaKE0ZlKTUlduHAh9evXZ+bMmXTt2pV69eqRlpZWXX0TERERERGRGlSpEcbrr7/e+Pzss89WeWdERERERERcTVNSnatQwhgbG4ubm5vT44sXL66yDomIiIiIiLiSXqvhXIUSxgEDBlR3P0REREREROQaU6GE8eGHH7bbv3DhAg0aVGo2q4iIiIiIyDWptFQjjM5U6qE3u3btIjAwkJtuugmAbdu2MWnSpGrpWG0VEhLCjBkzAEhNTcXf35+wsDBiYmIoLi4GoKCggEaNGrF582bjvPj4eIKCgvh//+//MX78eADGjx+PyWSidevWBAUFYTKZyMzM5KabbmLq1KnGuf3792fo0KF27ZhMJkwmE/v27SM1NRUfHx/OnDkDwOTJk7FarTzwwAOYTCaaNWtG7969MZlM7N69m7i4OMLCwujZsyfvvPOO02v99NNP8fb25ty5cwBMmTLF7iFJ//73v4mOjgZg06ZN9OnTB5PJRFhYGOnp6f/zvRYRERERqQi9h9G5SiWMjz/+OHPmzMHHxweAO+64g5UrV1ZLx2qjAwcOcOONN7J27VqjLDExkQ0bNtC+fXvMZjMAGRkZJCQklHkC7fz589m0aRP79u3j22+/5e2338ZqtRIdHc38+fOxWq2Eh4fTsmVLtm/fDsCpU6coKCgo047VasVqtXLLLbcA0Lp1a9577z27ep988glWq5XAwEBWr16N1Wpl3759tG7dmg0bNvDFF18QGxvr9HrT0tIYNWoUn376KQDDhg0zrvHy8djYWI4fP84zzzzD4sWLsVqtrF27ljZt2pR7L4uLiykoKLDbLifcIiIiIiJSNSqVMBYWFhIaGmrsu7m56T2MlWA2m3nwwQfp2LEj+/btszt22223kZeXB8CyZcuYPn06ubm5ZZ7YdPHiRU6fPn3FJzkFBASwe/duMjIyGDhw4BX7Fh8fzwcffMCFCxfKrde4cWNyc3P5+eefAWjevLnDehcuXCAvL49nn33WGC0MDAxkz549xojjsmXLuP/++1m5ciUjR46kZcuWALi7uxMSElJuP5KTk/H29rbbXpnz5hWvU0RERETkt2w2W5VttU2lEsYGDRpQUlJiPDE1Ly+PevUq1USdtnbtWiIjIxk5cmSZ0cOsrCwCAgIoLCzEZrPRvHlzQkNDyc7ONuokJCTQvn17OnXqxG233VZurCFDhpCens7KlSu577777I4lJCQYU1JPnz4NQMOGDYmJiWHhwoXltnvPPfcQExPDsGHD6NatG1988YXDeuvWraNv3760bduWkydPGqN/9957LxaLhZ9++ommTZvSsmVLDh8+jJ+fHwAWiwWTycSgQYPK7UdSUhL5+fl226QJT5R7joiIiIiII6XYqmyrbSqV7U2YMIHBgwdz7Ngxpk2bRlhYGM8880x19a1WycvLIzc3l4EDB5KcnMyKFSsASElJISwsjBYtWjBo0CAyMjLYs2cP0dHRrFu3zi6xnD9/Pt9++y379+83Rumc6dmzJ1arFTc3N5o0aWJ37NdTUn997PHHH+ftt9++4m9Gxo8fT3Z2Nmaz2emfv9lsZvny5URHR/Pvf//bmJYaGxtLeno6ZrPZWFfp5+fHwYMHAYiOjsZqtXLo0KFy++Dh4UHTpk3tNg8Pj3LPERERERFxRCOMzlXqUacPPvggN998M8uWLePMmTO8//779O7du7r6VquYzWZSUlIYPHgwAI888ggXLlwgMTGRCRMmGPXS09NZv369MT3TZDLZ/eA1a9aM+++/n3/961/Gw28ccXNz44EHHuDmm2+ucB+bN29Or169WLVqFffee6/DOocPH6Zp06Z4enoaa1l/68KFC/zwww9kZmYCl9ZuTpkyhUGDBtGjRw++++479u7da6x/ve+++xg4cCCjRo3Cx8fnitNiRURERETENSqcMF68eNFY53al9WVSVnp6OsuWLTP2+/Tpw08//YSXl5dRVlhYyKFDh4xkEeDWW2+1m5YK8NBDD9GnTx8ee+wxY3qwI48++igA+/fvtytPSEjA09MTgNmzZ9sd+/Of/8ycOXOctnngwAH+9Kc/4e7uzoULF5g+fXqZOuvXr+f222839tu1a8f3339PcXExHh4e9OvXj23bthnX2bJlS1599VWGDRsGQL169fjTn/7ktA8iIiIiIlWpNo4MVpUKJ4z169c3pg1K5WVlZdntjxo1qkwdLy+vMsnh5ddW9OrVyyhr1qwZ27ZtM/ZTU1Ptztm6davdfocOHYynk/62Llx6GM1l7dq14/z583bHrVar8fnuu++2e92HI/369aNfv352ZV9++aXxOTk5ucw5oaGhrFu3rtx2RURERESqg17D6FylpqT27duXxx57jISEBLu1b126dKnyjsnvR1JSElu2bDH2o6OjmTx5cg32SEREREREqkKlEsZ3330XuPQky8vc3Nz497//XbW9kt8VRyOGIiIiIiK/F5qS6lyFE0abzcamTZuM1x+IiIiIiIjUBkoYnavUazV++z4/ERERERGR37tSm63Kttqmwgmjm5sbHTt25Pjx49XZHxEREREREblGVGoNo6enJz169GDAgAF2D7159dVXq7xjIiIiIiIirlAbRwariputEhN2X3zxRYflU6dOrbIOiYiIiIiIuNL2nw5VWVs9bqxdz3ypVMIoIiIiIiJS2yhhdK5SU1ILCwuZMmUKa9aswc3NjX79+vG3v/0NLy+v6uqfSIWV5B10eUz3tjewNzTKpTH9N37KyY/SXBoToPmoWArXZro0plefcE5bN7o0JkATUyjnvt/j0pgNO93Kf16Z7dKYAK0m/Yn/vPqGa2NOfBKgRn6eaupaC1Z86tK4TQdEcXJhuktjNh85BICi7K0ujesZfJfLvxObj4oFoGhLjkvjeva6m72973VpTP+s1cClfwO6kpeXFyfOnHNpzBaNGwJQ+Pl6l8b16ncP53864NKY193YDoCjs+e6NK7vnx5zabzK0JRU5yr1lNTx48dz/vx5Fi5cyEcffURJSQnjx4+vrr6JiIiIiIhUO5ut6rbaplIjjLm5uXz99dfG/ttvv0337t2rvFMiIiIiIiJS8yqVMF68eJHCwkJjCmpRURGlpaXV0jERERERERFX0GNdnKtUwvjQQw8RHBzMH/7wB9zc3Pj44495+OGHq6tvIiIiIiIi1U5rGJ2rVMI4ceJEunXrxpo1a7DZbLzyyitER0dXV99ERERERESqnUYYnatQwlhQUMCJEyfo0KED0dHRRpL4448/UlBQQNOmTau1kyIiIiIiIuJ6FXpK6sSJE9m2bVuZ8o0bNzJ58uQq75SIiIiIiIir6CmpzlUoYdywYQNDhgwpUx4XF8eGDRuqvFMiIiIiIiKuUmqzVdlW21QoYaxfv77TY25ublXWGREREREREbl2VChhvHDhAgUFBWXK8/PzKSkpqfJO1QZZWVlEREQQFhZGREQEO3fuJC0tjd69e9O7d28effRRzp07B0B8fDwmk8k499VXX8XX1xeA1NRUPD09KSoqAiAnJwc3Nzd27twJgNVqJTw8nLCwMEaMGMHJkycBmDZtGl27djUW8I4YMYL9+/cbMUJCQpgxY4axn5qair+/PyaTiX79+nHq1Cmjb0FBQZhMJkwmE/v27QMurWtt1KgRmzdvLvc+7Nmzh/r163Pw4EEA3nnnHWbOnGkcLyoqokePHgDs2rWL++67D5PJRGhoKG+//XYl7riIiIiIyNWx2WxVttU2FUoYR44cSVxcnJGMAJw8eZKEhARGjBhRbZ37vTp+/DiTJk3CbDazYcMGFi1axMWLF5k7dy5r1qwhKyuLG264gb/97W/GOefOneOXX34BYNOmTbRr18441qVLF1avXg2A2WwmKCgIgBMnTjBp0iSWLl3Khg0b6N+/P0888YRxnpubG8uXLy/TvwMHDnDjjTeydu1au/LExESsViv33HMPH374oVE+f/58rFYrVquVW265BYCMjAwSEhJIS0sr914sXryYcePGYTabAXjggQdYtmyZcXzFihUMGDCA8+fP88gjj/D2229jtVrJysritttuc9pucXExBQUFdltxcXG5fRERERERcURTUp2rUML47LPP0qxZM9q1a0ePHj3o0aMH7dq1w8vLi+eff766+/i7s3LlSkaOHEmLFi0A8PX1ZdmyZSQmJuLh4QHAM888Y5dsDR48mKVLl/Lzzz/Ttm1b6tX77x9NTEwMGRkZwKVRuC5dugCXkq1Ro0bRvHlz4NKa0i1btnDx4kXgUgL4+uuvl+mf2WzmwQcfpGPHjsaI4a+dOnXqir8dWbZsGdOnTyc3N7fcuhs2bGDmzJl8+umnAPj4+NC4cWMOHDhg9CU2Npbs7GxCQkLo0KEDcCnZDQsLc9pucnIy3t7edtsrb80pt88iIiIiIo4oYXSuwmsY33//fb7++mumTJnClClT+Prrr3n//ffLXd9YVx0+fBg/P78yZW3btjX2GzZsaDedt1+/fqxZswaz2cwDDzxgd26zZs04e/YsW7ZsMaZvOmoToFWrVhw7dgyANm3a0LFjR7KysuzqrF27lsjISEaOHGmXtKakpNC1a1c+//xz4uLijPKEhARjSurp06cpLCzEZrPRvHlzQkNDyc7Odngf9uzZQ0BAAJ6enlx//fXGtNShQ4eSnp7O2bNn+eGHH+jWrZvdPduxYwcmk4m7777byR2GpKQk8vPz7bZJj09wWl9ERERERCqvQgnjZR07diQ2NpbY2Fg6duxYXX363fPz8zOSI2dl586d47rrrjP2r7vuOjw9PVm+fDnh4eFl2oyMjGT8+PF2T6t1FOfo0aP4+PgY+88884zdmsG8vDxyc3MZOHAgycnJrFixwjiWmJjI119/TefOncnLyzPKfz0ltUmTJmRkZLBnzx6io6NZt26d02mpaWlp5OTkEB0dzTfffEN6ejrw32mpq1at4r777itzLYGBgVitVkpLSx22C+Dh4UHTpk3ttsujtyIiIiIilaE1jM5VKmGUiunfvz8ff/wxJ06cAODYsWMMGjSIN99801hnN2vWLGJjY+3OGz16NDExMTRo0KBMm4MHDyYoKIjAwEC7OAsXLjQeUPPRRx/Rq1cvu1HfgIAA3N3djYfkmM1mUlJSsFgsfPbZZ3Tq1MluWmqDBg2YPHkyf/3rX51eX3p6OuvXr8disZCVlcVXX33l8C/HmjVr2LJlCxaLhU2bNrFy5Urg0rRUd3d35syZY9yDnj17smXLFuPBPDabzZhaKyIiIiJSnZQwOlc2M5H/WYsWLXjllVcYOnQoFy9exN3dnTfeeIOxY8fSr18/ADp37kxKSordeWFhYU7X7fn4+PDOO++UiZOcnExMTAw2m402bdowb968MudOnDiR4OBg4FKy9+uHzvTp04e0tDTatGljlHXr1o1Dhw4Zo4wJCQl4enoC8NJLL3Ho0CFatmxp1L/11lvJzs6mV69eRtnevXvx9vY21mJ6eHjg5ubGwYMHueGGGxgyZAivvfYa3bt3By6NsP7zn//kscce49y5c9SvX99uWqyIiIiIiLieEsZq0rt3b9atW2dX1qVLF4YNG1ambmpqapmyrVu3Apdea1FefZPJRGZmZpk606ZNMz737NnT+G3Hb9czjho1ymH/L9dz1Lffrln8bSIL4O/vz9KlS+3KLBaL8XncuHGMGzfO7vivnwYrIiIiIuIqpbVvYLDKKGGUKpGSksKSJUuM/cDAQGbPnl2DPRIRERERqZjaOJW0qihhlCqRmJhIYmJiTXdDRERERESqkBJGERERERGp0zTC6JwSRhERERERqdNKUcLojBJGERERERGp0zTC6JzewygiIiIiIiIOaYRRRERERETqNL1Wwzk3m8ZfRURERESkDlv+1XdV1tbAOzpXWVvXAo0wSq1RWFjo8pheXl4UbclxaUzPXndz9M1/uDQmgO8T4zj37fcujdmwayeK9/7g0pgAHv4dKTnyi0tjure+noIVn7o0JkDTAVHkZ6xyaUzvQfcB1EhcV9/jpgOiADj/0wGXxr3uxnY18t0EsLf3vS6N65+12uXfib5PjANgb2iUS+P6b/yUouytLo3pGXwX4Pr/x3p5edVITKiZ76YTqR+5NGaL+FEANfadKL8vShhFRERERKRO06RL5/TQGxERERERqdNsNluVbdXl5MmTxMXF4e3tjbe3N3FxcZw6darC13fvvffi5ubG0qVLKxVXCaOIiIiIiNRppdiqbKsuo0aNYseOHVgsFiwWCzt27CAuLq5C586ePRs3N7eriqspqSIiIiIiItew7777DovFQnZ2Nj179gTg3XffpVevXuzevZuAgACn53799dfMmjWLL7/8kjZt2lQ6thJGERERERGp06pyKmlxcTHFxcV2ZR4eHnh4eFx1m1u2bMHb29tIFgGCg4Px9vZm8+bNThPGM2fOMHLkSObMmUPr1q2vKrampIqIiIiISJ1ms1XdlpycbKwzvLwlJyf/T/07cuQIrVq1KlPeqlUrjhw54vS8P//5z4SEhBATE3PVsTXCKCIiIiIiUkWSkpJ46qmn7MqcjS5OmzaNF198sdz2vvzySwCHaxBtNpvTtYkZGRmsW7eO7du3V6TbTilhFBERERGROq20CqekVmb66YQJExgxYkS5dTp06EBubi6//FL2HdFHjx7l+uuvd3jeunXr+OGHH2jWrJld+ZAhQ+jduzdWq7VCfVTCKCIiIiIidVpNvYfRx8cHHx+fK9br1asX+fn55OTkcPfddwPwxRdfkJ+fT0hIiMNzJk+ezJgxY+zKbr/9dl5//XUGDhxY4T5qDWMVi46OZteuXQD8+OOP9O3bF19fX0wmEyaTieeee86om5SURFRUlLG/f/9+o25wcDDbtm0DIDc3l7CwMMLDwwkODubAgQMOY3t5eWEymQgKCmLWrFkApKamMmfOHKPO5MmTsVqt7N+/n6FDhwIQHx/Pzp07y7QVERFBREQEY8eO5cSJEw5jpqam4unpSVFREQA5OTm4ubkZ7VmtVsLDwwkLC2PEiBGcPHkSuDT8fvvttxMREYHJZGLZsmVlrsNkMpX5IRcRERERqWs6d+5MdHQ0Y8eOJTs7m+zsbMaOHcuAAQOMB94cPHiQTp06kZOTA0Dr1q257bbb7DaA9u3bc9NNN1U4tkYYq9hrr73GX/7yF1atWsXEiRN59dVXmTFjBmazuUzdnJwcvLy8OH78OC1btgQgPDwcs9nM5s2befnll0lLS+Oll15i7ty5dO3albNnzzqdpxwQEIDVaqW0tJTu3buTmJh41dcREBDAunXrAHjvvfd47LHHWLRokcO6Xbp0YfXq1QwdOhSz2UxQUBAAJ06cYNKkSVgsFpo3b84HH3zAE088wYIFC4BLC4IHDBjA8ePHiYyM5Oabb+b22283rqM8jp4+df78+f/p6VMiIiIiUjfV1AhjZXz44Yc8+eSTREZGAjBo0CC7gaGSkhJ2797NmTNnqjSuRhirWNeuXfH39+epp56iefPmtGjRwmG97du3c8cddzBs2DCWLFlS5vipU6eMH9zGjRuzfv16ioqKaNSoEQ0bNiy3D+fPn8fd3Z169armj3fMmDFs27aNixcvOjweExNDRkYGALt27aJLly4ArFixglGjRtG8eXMA4uLi2LJlS5l2WrZsyeOPP87SpUsr3CdHT5967bXXrubyRERERKSOK7XZqmyrLi1atGDBggUUFBRQUFDAggUL7NYndujQAZvNhslkctqGzWbj/vvvr1RcJYzV4MUXX2TBggVMnz4dgMzMTGOK5eXfAqSlpTF8+HBiYmJYuXKlcW5mZiY9e/bkoYce4oUXXgBg5syZfPvtt3Tv3p0RI0Y4/a3B7t27MZlMBAQE0L9/f6cjkVfDx8eHY8eOOTzWrFkzzp49y5YtW+jRo4dRfvjwYdq2bWtXt1WrVg7b8fPz49ChQ3bX8dspvL+WlJREfn6+3fb0009f7eWJiIiISB32e0gYa4qmpFaDZs2a0b59e3x9fSkqKjKmmf7aqlWr+Oqrr4BLo3LHjx8H/jslNSUlhezsbLp160arVq2YO3cuAFOnTuWDDz5g3LhxZeJensp54cIFwsPDGTNmDA0bNjTaBjh37hyNGjWq9DUdO3as3AW5kZGRjB8/nvnz5zN79mzgUhJ48OBBu3pHjx512M6hQ4fw8/Ozu47yOHr6VGFhYUUuRUREREREKkgjjDVgx44dhIeHY7FYsFgsTJ8+vcx0zPHjxzNv3jxKSkrYu3evUd6qVStKS0vLbb9BgwY0atSIU6dOcdttt7Fx40YALl68yPbt2/H3969Uf+fPn89dd91F/fr1ndYZPHgwQUFBBAYGGmX9+/dn4cKFnDp1CoCPPvqIXr16lWnnxIkTzJ07l8GDB1eqXyIiIiIiVcFms1XZVttohNEFLk9JBQgMDMTT05N77rnHON6nTx9Gjx5Nnz59jDJ3d3eioqJIT09n7969LF++nMaNG9OsWTPjoTG/dXkqZ3FxMT169KB79+5GzNDQUGw2G4888ggtWrSgoKDA7tyxY8fi6ekJXFpQu3v3biIiIgC4+eabeeutt8q9Rh8fH9555x27shYtWpCcnExMTAw2m402bdowb94843hSUhKvv/46Fy9e5LnnnjOe3HT5OuDS050+/vjjcmOLiIiIiPwvSmtfnldllDBWk61btwKXFp8ePXq03Lp+fn5YLBYAu6mrycnJxufnn3/+ijGdTcmcOnUqU6dOtSvr0KGDESs1NbXCbf1WfHx8mbJft2cymcjMzCxTZ9q0aUybNs1hm5paKiIiIiJybVDC+DuUn59PTEyMXdns2bPtpoNWh0WLFhlrKUGjfyIiIiJSO9TGqaRVRQnj75C3t/cVHwpTHYYPH87w4cNdHldEREREpDopYXROCaOIiIiIiNRptfF1GFVFT0kVERERERERhzTCKCIiIiIidZoGGJ1TwigiIiIiInWa1jA6pympIiIiIiIi4pCbTem0iIiIiIjUYe+sza6ytv7YJ7jK2roWaEqq1BolBw+7PKb7DW04+vpbLo3p++fHOTR5mktjAvi9PI0THyxyacwWccM5bd3o0pgATUyhnP85z6Uxr2vflv0jRrs0JkCHj//Fj0MfcmnMm8z/B8Cxt//p0rg+4x/hp4cedWnMG/9vHgAnF6a7NG7zkUNq5LsJIH+5xaVxvQdGc2jSVJfG9HvlRQAKVnzq0rhNB0RxNGWeS2P6Jl76O1NYWOjSuF5eXpw4c86lMVs0bgjAsXn/cmlcn0dHu/zfMO43tAFgb2iUS+P6b3Tt35nK0Biac5qSKiIiIiIiIg5phFFEREREROo0vYfROSWMIiIiIiJSpylhdE4Jo4iIiIiI1Glaw+ic1jCKiIiIiIiIQxphFBERERGROk0DjM4pYRQRERERkTpNaxid05RUERERERERcUgjjCIiIiIiUqfpoTfOKWEUEREREZE6TQmjc5qSeo3Yv38/vr6+mEwmTCYTzz33HHfddZddHavVSrt27Yw6c+bMYdSoUXz55ZdGnbVr1/Loo48Cl37w27Zty0cffQRARkYGJpOJwMBAo52kpCRKS0t57rnnCA8PJyQkhDfeeMNoz9/fH5PJRFBQEKmpqQAUFBQwcOBATCYTd999N8uXL3d6Xf/4xz8ICAgw9svr77JlywgPD8dkMhEREUFmZuZV3k0RERERkYortdmqbKttNMJ4DQkPD8dsNhv7FoulTJ3hw4fz97//3dhv27YtZrOZoKAgAMxmM7GxsQBs2rSJ++67jyVLljBq1CgGDRrEoEGDsFqtrFixwmjn3XffpaSkhMzMTEpKShg0aBBdunShb9++eHt7Y7VaOXfuHF27diU+Pp4PPviA6OhoHn/8cWw2G/n5+U6vacWKFfTq1Yuvv/6a7t27M2zYMIf9/f7773nrrbdYvXo1jRs35vTp0+zevdtpu8XFxRQXF9uV1SsuxsPD40q3WUREREREKkgjjL9z0dHRfPbZZwCUlpayceNGTCYTAGlpaSQmJlJcXExhYaHTNj7++GOSkpIAcHd35+mnn2bhwoV2dU6fPk1JSQkAjRs3Jicnh19++QU3NzeaNWvmsN1jx47RpEkT/vjHP5KWllZufxcvXswTTzxB48aNAWjSpAl33nmn0z4nJyfj7e1tt70y580r3S4RERERkTJsVbjVNkoYryGZmZl2000dWbRokVHHbDbTsGFDOnfuzI4dO8jKyiIkJIT69etjs9nYvXs3Xbt2ZfDgwaxYscJp3MLCQrukr127dhw6dAiA/Px8wsPDuemmm3jppZcAiIuLIyAggKioKEJCQti7d6/Ddj/55BOGDBlCr169jGmozvp7+PBh/Pz8AEhNTSU0NJTx48c77XNSUhL5+fl226QJT5Rzd0VEREREHNOUVOc0JfUa8tspqZfXDP7ab6ekAsTGxpKens6pU6eM6aibN2/mhx9+IDo6mvPnz+Pj48PIkSMdxvXy8iI/Px9vb28A8vLyjOTN29ubzMxMvvrqK15//XUefvhhGjRowJQpU5gyZQrr16/nhWoVd/YAACAASURBVBdeKDMiCZfWJBYXF/Pee++xd+9ecnNz6datm8P++vn5cfDgQe68807i4+MxmUz85S9/cXqvPDw8ykw/LSksclpfREREREQqTyOMtcC9996LxWJhw4YNdtNRFy9ejMViYd26dZw5c4bTp087PH/kyJG88sorAFy4cIFZs2YxatQouzp33HEHZ86cYdeuXfz000+cP38egFatWlFaWlqmzWPHjtGwYUPWrFmDxWLh/fffN6alOurvsGHDePPNNzlz5ozRDxERERERV7DZbFW21TYaYbyGXJ6SChAYGEhBQQF9+/YFoHXr1owZM4ZFixaxdetW4NJ6wMmTJ9OwYUNuueUWvLy8aNCgATabjXXr1jFr1iyj7ZCQEJYvX+5wlHH06NE8//zzhIeHU1JSwogRI+jTp0+Zen/84x9544036N+/P8OGDaNRo0YADqfPLlmyhN69exv7wcHB/OlPf+Kll14q01+AgIAAHn/8caKjo3F3d6dBgwY89dRTV3knRUREREQqrrS09iV6VUUJ4zWiQ4cOHD161K5s9uzZZeodOHDA4fm/nhLq5uZGbm6u3fEpU6YYny+vgbysXr16TJ8+3WG7l5NTgKioKKKiogAYOHCgkyu5ZOzYsXb77u7ubNu2zWF/L7v//vu5//77y21XRERERERcRwmjVImEhAR+/PFHYz8+Pp74+Pia65CIiIiISAXVxqmkVUUJo1SJ+fPn13QXRERERESuSm18umlVUcIoIiIiIiJ1mtJF5/SUVBEREREREXFII4wiIiIiIlKnaQ2jc0oYRURERESkTtMaRufcbEqnRURERESkDptm/rTq2hoaVWVtXQs0wii1RsnBwy6P6X5DG/b2vtelMf2zVnNq0ScujQnQbPgDFK7NdGlMrz7hnLZudGlMgCamUIp373NpTI+AW/jPq2+4NCZAq4lPujxuq4lPArj8z7aJKZT//P1Nl8Zs9ZcnAMhfbnFpXO+B0ZxcmO7SmM1HDgHgTM62K9SsWo3vvpNTi5e4NGazYYMBKMreeoWaVcsz+C72hrr2H6L+Gy/9I7qwsNClcb28vDhx5pxLY7Zo3BCA0+s2uDRuk4gwzv+c59KY17VvC8DRlHkujeub+KhL41WGxtCcU8IoIiIiIiJ1mqakOqeEUURERERE6jTli87ptRoiIiIiIiLikEYYRURERESkTtMaRueUMIqIiIiISJ2mNYzOaUqqiIiIiIiIOKQRRhERERERqdM0wuicEkYREREREanTtIbROU1JFREREREREYc0wigiIiIiInWaRhid0wjjVcrKyiIiIoKwsDAiIiLYuXMnaWlp9O7dm969e/Poo49y7tw5AOLj4zGZTMa5r776Kr6+vgCkpqbi6elJUVERADk5Obi5ubFz504ArFYr4eHhhIWFMWLECE6ePAnAtGnT6Nq1q/HDPWLECPbv32/ECAkJYcaMGcZ+amoq/v7+mEwm+vXrx6lTp4y+BQUFYTKZMJlM7Nu3D4CCggIaNWrE5s2bnd6DadOmcdNNNxn7ixcvxs3NjdOnTwOUez+CgoLo06cPkZGRbNy4EYD9+/fj6+tr9OW5556rzB+JiIiIiMhVKbVV3VbbKGG8CsePH2fSpEmYzWY2bNjAokWLuHjxInPnzmXNmjVkZWVxww038Le//c0459y5c/zyyy8AbNq0iXbt2hnHunTpwurVqwEwm80EBQUBcOLECSZNmsTSpUvZsGED/fv354knnjDOc3NzY/ny5WX6d+DAAW688UbWrl1rV56YmIjVauWee+7hww8/NMrnz5+P1WrFarVyyy23AJCRkUFCQgJpaWnl3gsfHx+2bt1qnNO9e3cAdu/eXe79mD9/PmvXruXdd99l7Nixxr0JDw83+vLr+r9VXFxMQUGB3VZcXFxuX0VEREREHLHZbFW21TZKGK/CypUrGTlyJC1atADA19eXZcuWkZiYiIeHBwDPPPOMXbI1ePBgli5dys8//0zbtm2pV++/tz4mJoaMjAwAdu3aRZcuXQBYsWIFo0aNonnz5gDExcWxZcsWLl68CFxKAF9//fUy/TObzTz44IN07NjRGDH8tVOnTl3xh3nZsmVMnz6d3NzccusOHTqU9PR0zp49y/nz52nWrBkAixYtKvd+XHbjjTcSGxvLZ599Vm5/fis5ORlvb2+77ZU5b1aqDRERERERKZ8Sxqtw+PBh/Pz8ypS1bdvW2G/YsCElJSXGfr9+/VizZg1ms5kHHnjA7txmzZpx9uxZtmzZQo8ePZy2CdCqVSuOHTsGQJs2bejYsSNZWVl2ddauXUtkZCQjR460S9JSUlLo2rUrn3/+OXFxcUZ5QkKCMQ309OnTFBYWYrPZaN68OaGhoWRnZzu9F127duXbb79l9erVREVFVfh+/Jqfnx+HDh0CIDMz0+jLnDlznMZNSkoiPz/fbps04Qmn9UVEREREnNEIo3NKGK+Cn58fBw8eLLfs3LlzXHfddcb+ddddh6enJ8uXLyc8PLxMm5GRkYwfP54hQ4aUG+fo0aP4+PgY+8888wwzZ8409vPy8sjNzWXgwIEkJyezYsUK41hiYiJff/01nTt3Ji8vzyj/9ZTUJk2akJGRwZ49e4iOjmbdunVXnJZ6++23k5ycTExMTIXvx68dOnTISMB/PSV1woQJTmN6eHjQtGlTu+3yaKaIiIiISGWU2mxVttU2ShivQv/+/fn44485ceIEAMeOHWPQoEG8+eabxjq6WbNmERsba3fe6NGjiYmJoUGDsg+nHTx4MEFBQQQGBtrFWbhwofGAmo8++ohevXpRv359o05AQADu7u7GQ3LMZjMpKSlYLBY+++wzOnXqZDcttUGDBkyePJm//vWvTq8vPT2d9evXY7FYyMrK4quvvir3tyUPPvggkZGRdonssGHDrng/4NJ6y/T0dCIjI522LyIiIiIiNUOv1bgKLVq04JVXXmHo0KFcvHgRd3d33njjDcaOHUu/fv0A6Ny5MykpKXbnhYWFERYW5rBNHx8f3nnnnTJxLo/c2Ww22rRpw7x588qcO3HiRIKDg4FLyd6yZcuMY3369CEtLY02bdoYZd26dePQoUPGKGNCQgKenp4AvPTSSxw6dIiWLVsa9W+99Vays7Pp1auXw7537tyZ6dOn25UFBASUez8SEhJo2rQp9evXZ968eVx//fXs37/fmJIKEBgYyOzZsx3GFBERERGpKrVxKmlVUcJ4lXr37s26devsyrp06cKwYcPK1E1NTS1TdvnJovHx8eXWN5lMZGZmlqkzbdo043PPnj2NH/LfrmccNWqUw/5frueob79ds/jbRNZRHy6zWq3G52HDhlX4fgB06NCBo0ePOjwmIiIiIlJdauPrMKqKEkapkJSUFJYsWWLsa/RPRERERGqLUltpTXfhmqWEUSokMTGRxMTEmu6GiIiIiIi4kBJGERERERGp07SE0TkljCIiIiIiUqfpoTfO6bUaIiIiIiIi17iTJ08SFxeHt7c33t7exMXFGa/fc+bIkSPExcXRunVrPD09ueOOOzCbzZWKq4RRRERERETqtFKbrcq26jJq1Ch27NiBxWLBYrGwY8cO4uLiyj0nLi6O3bt3k5GRwTfffMMDDzzA8OHD2b59e4Xjutk0/ioiIiIiInXY2H8sqrK23h03vMrauuy7776jS5cuZGdn07NnTwDjPenff/89AQEBDs9r0qQJc+fOtUssW7ZsyauvvsojjzxSodgaYRQRERERkTrNZrNV2VZcXExBQYHdVlxc/D/1b8uWLXh7exvJIkBwcDDe3t5s3rzZ6XmhoaEsWrSIEydOUFpayscff0xxcTEmk6nCsfXQG6k19va+1+Ux/bNWU5J30KUx3dve4PKYl+MWFha6NKaXlxcXjh5zaUyABr4+NXKtJUd+cWlMAPfW11Ny8LBrY97QBoCSQ0dcG9evdY3EBGrk56kmYkLNXGtNfA9DDV1rHfr7WlM/w67+LnZvfX3NXWsN/TzVdsnJybz44ot2ZVOnTmXatGlX3eaRI0do1apVmfJWrVpx5Ijzv5+LFi1i+PDhtGzZkgYNGtC4cWOWLFlCx44dKxxbI4wiIiIiIlKnldqqbktKSiI/P99uS0pKchh32rRpuLm5lbtt3boVADc3tzLn22w2h+WXPffcc5w8eZI1a9awdetWnnrqKWJjY/nmm28qfG80wigiIiIiInVaVT7WxcPDAw8PjwrVnTBhAiNGjCi3TocOHcjNzeWXX8qOfh89epTrr7/e4Xk//PADc+bMYefOnXTt2hWA7t27k5WVxVtvvcW8efMq1EcljCIiIiIiIjXAx8cHHx+fK9br1asX+fn55OTkcPfddwPwxRdfkJ+fT0hIiMNzzpw5A0C9evaTSuvXr09paWmF+6gpqSIiIiIiUqeVYquyrTp07tyZ6Ohoxo4dS3Z2NtnZ2YwdO5YBAwYYT0g9ePAgnTp1IicnB4BOnTpxyy23MG7cOHJycvjhhx947bXX+Pzzz7n//vsrHFsJo4iIiIiI1GlV+ZTU6vLhhx9y++23ExkZSWRkJN26deODDz4wjpeUlLB7925jZNHd3Z1Vq1bh6+vLwIED6datG//3f//H+++/z3333VfhuJqSKiIiIiIico1r0aIFCxYscHq8Q4cOZRJWf39/0tPT/6e4ShhFRERERKROKy2tvpHB3zsljCIiIiIiUqdV51TS3zsljCIiIiIiUqdpgNE5PfSmlgoJCWHGjBkApKam4unpSVFREQA5OTm4ubmRm5uLyWTCZDLh5eVlfM7Pzy/T3rfffkt0dDTh4eGEhoaSmZnJ/v378fX1xWQyERwczLZt24x4/v7+Rntms9moGxERQXh4OBMnTuTs2bMAxMfHV6ovIiIiIiLiGhphrIUOHDjAjTfeyNq1a5kyZQoAXbp0YfXq1QwdOhSz2UxQUBD16tXDarUCcNdddxmff+v8+fOMGTOGRYsW0b59e4qKiti5cycA4eHhmM1mNm/ezMsvv0xaWhoAiYmJTJgwwWhj//79Rl2bzcYLL7zAtGnTeOWVVwAq3JfLiouLKS4utu9naSnX1dPvQERERESkcjQl1Tn967oWMpvNPPjgg3Ts2JF9+/YBEBMTQ0ZGBgC7du2iS5cuFW4vOzubkJAQ2rdvD4Cnpyc9e/a0q3Pq1KkK/0Vzc3PjueeeY9myZRXuw28lJyfj7e1tt8078MNVtyciIiIidZetCv+rbZQw1kJr164lMjKSkSNHGiN+zZo14+zZs2zZsoUePXpUqr3Dhw/j5+fn8FhmZiY9e/bkoYce4oUXXjDKU1JSjGmlGzduLHOeh4dHmRHCykhKSiI/P99ue7Rdx6tuT0REREREylLCWMvk5eWRm5vLwIEDSU5OZsWKFcaxyMhIxo8fz5AhQyrVpp+fHwcPHnR4LDw8nC+++ILnn3+e7OxsozwxMRGr1YrVaiU0NLTMeefPn8fDw6NS/fg1Dw8PmjZtardpOqqIiIiIXI1Sm63KttpG/8KuZcxmMykpKVgsFj777DM6derEhQsXABg8eDBBQUEEBgZWqs3g4GC2bNnCzz//DMCZM2fIycmxqzN+/HjmzZtHSUlJhdqcMWMG999/f6X6ISIiIiJSHWw2W5VttY0SxlomPT2d8PBwY79Pnz4cPXoUAB8fH955551Kt+nu7s57773HH//4R8LDw4mOji4zndTd3Z2oqCjS09MB+ympqampwKXpq5efklpUVMTUqVOv8ipFRERERKpOqa3qttpGT0mtZbKysuz2R40a5bDe5STusq1bt5bbbteuXbFYLGXKzWaz8Tk5Odn4HB8fX6bu5cT1f+2LiIiIiIi4hhJGsZOfn09MTIxd2ezZsys9jVVERERE5PeiNk4lrSpKGMWOt7f3Fd+BKCIiIiJSmyhhdE5rGEVERERERMQhjTCKiIiIiEidVhtfh1FVlDCKiIiIiEidpoTROU1JFREREREREYc0wigiIiIiInWaHnrjnJtNd0dEREREROqw+5LfqbK2ViX9scrauhZohFFqjcLCQpfH9PLy4rR1o0tjNjGFciZnm0tjAjS++072ht3n0pj+G1Zx7rvdLo0J0LBzABeOHnNpzAa+Phx9/S2XxgTw/fPj/Oe1OS6N2erpCQCc2/mdS+M2vK0zJz9c7NKYzf8wDKBG7vGxt//p0pg+4x8B4Nz3e1wat2GnW13+ndj47jsBXP791LBzAKfXbXBpzCYRYYDr/x/r5eVVIzGhZr6bauL/OQAnP0pzadzmo2JdGq8ytIbROa1hFBEREREREYc0wigiIiIiInWaVuk5p4RRRERERETqNE1JdU5TUkVERERERMQhjTCKiIiIiEidpimpzilhFBERERGROk35onNKGEVEREREpE7TGkbntIZRREREREREHNIIo4iIiIiI1Glaw+icEkYREREREanTrNMm1HQXrlmaklpD9u/fz9ChQ+3K9uzZQ/369Tl48KBRZjKZeOyxx4z94OBgAFJTU/H39yciIoKwsDDee+89u7aSkpKIioqyi+fr64vJZCI4OJht27bZtWMymTCZTJjNZuOckJAQZsyY4fQarFYr9erV48cffwTgyJEjNGjQgBUrVgDwzTff0K9fP8LDw+nfvz8HDhy4Yt9/3ZcBAwY4jV1cXExBQYHdVlxc7LS+iIiIiIhUnhLGa8jixYsZN26cXdIGsHXrVn755Zcy9RMTE1m3bh0Wi4W0tDRWr15tHMvJyaFRo0YcP37cKAsPD8dqtTJr1ixefvllu3asVitWq9VIYg8cOMCNN97I2rVry+3znXfeSXp6OgCffPIJd9xxBwAlJSWMGTOGf/7zn2RmZjJp0iT+8Ic/XLHv3t7eRl8uJ56OJCcn4+3tbbe99tpr5fZVREREREQqRwnjNWTDhg3MnDmTTz/91K58woQJpKSkOD2vcePGTJo0yUg0t2/fzh133MGwYcNYsmRJmfqnTp264jxts9nMgw8+SMeOHdm3b5/Ter1792bjxo0ArFmzhr59+wKwZcsWQkJCaN++PQBhYWGUlpYao4zO+l5RSUlJ5Ofn221PP/10pdoQEREREZHyKWG8RuzZs4eAgAA8PT25/vrr7aalDh48mLVr11JYWOj0fD8/Pw4dOgRAWloaw4cPJyYmhpUrVxp1MjMz6dmzJw899BAvvPCCUZ6SkmJMA72c/K1du5bIyEhGjhxJWlqa07j16tWjTZs2bN++naZNm3LdddcBcPjwYdq2bWtXt127dkYfnfU9Pz/f6MuYMWOcxvXw8KBp06Z2m4eHh9P6IiIiIiJSeXrozTUiLS2NnJwcoqOjOXbsGOnp6Tz55JPApaRszJgxzJs3z+n5hw4dws/PD4BVq1bx1VdfAbBr1y5jWmp4eDhms5mUlBSys7Pp1q0bcGl66IQJ/13om5eXR25uLgMHDqS0tJSioiKSkpKcxn7ggQcYPXo0U6dOZceOHcClJPCLL76wq5eXl4efnx/fffed075fnpIqIiIiIiI1TyOM14g1a9awZcsWLBYLmzZtshsZBHjooYdYtGgRJSUlZc49e/YsM2fOZOjQoezYsYPw8HAsFgsWi4Xp06ezdOlSu/rjx49n3rx5DtsCjKTSYrHw2Wef0alTp3Knpd5zzz1069aN6Ohooyw4OJjNmzeTl5cHwMaNG3Fzc6Ndu3ZO+y4iIiIiItcWjTDWoKysLPr27YvNZiMnJ4d69S7l7x4eHri5udlNS/Xw8CA2Npa5c+caZSkpKXzyySdcuHCBuLg47r33Xp599lnuueceo06fPn0YPXo0ffr0Mcrc3d2JiooyHlaTkpJirCGMj48nPT2dZcuW2bWRlpbmdJSxQYMGvP/++3Zl7u7uvPvuuyQkJHD+/HmaNGnCggULyu07/HdK6mUabRQRERERqTluNr2lUmqJ8tZ4VhcvLy9OWze6NGYTUyhncra5NCZA47vvZG/YfS6N6b9hFee+2+3SmAANOwdw4egxl8Zs4OvD0dffcmlMAN8/P85/Xpvj0pitnr40Bf7czu+uULNqNbytMyc/XOzSmM3/MAygRu7xsbf/6dKYPuMfAeDc93tcGrdhp1td/p3Y+O47AVz+/dSwcwCn121wacwmEWGA6/8f6+XlVSMxoWa+m2ri/zkAJz9y/pyK6tB8VKxL40nV0AijVEhmZiZTp061K9Pon4iIiIhI7aaEUSrk8jscRURERESk7tBDb0RERERERMQhJYwiIiIiIiLikBJGERERERERcUgJo4iIiIiIiDik12qIiIiIiIiIQ3pKqtQaNfUexqLsrS6N6Rl8F0ff/IdLYwL4PjGOc99+79KYDbt2onjvDy6NCeDh35GSI7+4NKZ76+spWPGpS2MCNB0QRX7GKpfG9B506X2eNRHX1fe46YAoAM7/nOfSuNe1b1sj300Ae0OjXBrXf+OnHE2Z59KYvomPArC3970ujeuftbrG3jlZl97DWBPfTSf+tcClMVuMfhCA/OUWl8b1Hhjt0nhSNTQlVURERERERBxSwigiIiIiIiIOKWEUERERERERh5QwioiIiIiIiENKGEVERERERMQhJYwiIiIiIiLikBJGERERERERcUgJo4iIiIiIiDikhFFEREREREQcUsIoIiIiIiIiDilhdKHo6Gh27doFwI8//kjfvn3x9fXFZDJhMpl47rnnjLpJSUlERUUZ+/v37zfqBgcHs23bNgByc3MJCwsjPDyc4OBgDhw44DC2l5cXJpOJoKAgZs2aBUBqaipz5swx6kyePBmr1cr+/fsZOnQoAPHx8ezcubNMWxEREURERDB27FhOnDjh9JoLCgpo1KgRmzdvBuCzzz7j8ccft6tz++23U1xcTF5eHkOHDsVkMhEaGsoLL7xQ/g0VEREREZFq1aCmO1CXvPbaa/zlL39h1apVTJw4kVdffZUZM2ZgNpvL1M3JycHLy4vjx4/TsmVLAMLDwzGbzWzevJmXX36ZtLQ0XnrpJebOnUvXrl05e/Ysbm5uDmMHBARgtVopLS2le/fuJCYmXvV1BAQEsG7dOgDee+89HnvsMRYtWuSwbkZGBgkJCaSlpRESEkJERARPP/00paWl1KtXjy+++ILAwEA8PDyIj4/n73//O4GBgQBkZmY67UNxcTHFxcV2ZefPn8fDw+Oqr0tEREREROxphNGFunbtir+/P0899RTNmzenRYsWDutt376dO+64g2HDhrFkyZIyx0+dOoXNZgOgcePGrF+/nqKiIho1akTDhg3L7cP58+dxd3enXr2q+aMfM2YM27Zt4+LFiw6PL1u2jOnTp5Obm4vNZqNBgwYEBwcbI45ms5nY2Fh+/vlnvL29jWQRLiXIziQnJ+Pt7W23vfbaa1VyTSIiIiIicokSRhd78cUXWbBgAdOnTwcujaJdnpJ6eXpoWloaw4cPJyYmhpUrVxrnZmZm0rNnTx566CFjuubMmTP59ttv6d69OyNGjODMmTMO4+7evRuTyURAQAD9+/d3OhJ5NXx8fDh27FiZ8sLCQmw2G82bNyc0NJTs7GwAYmNjjVHVzz//nKioKA4fPoyfnx8AR44cwWQy0alTJ6fXk5SURH5+vt329NNPV9k1iYiIiIiIpqS6XLNmzWjfvj2+vr4UFRUZ00x/bdWqVXz11VcA7Nq1i+PHjwP/nZKakpJCdnY23bp1o1WrVsydOxeAqVOn8sEHHzBu3LgycS9PSb1w4QLh4eGMGTOGhg0bGm0DnDt3jkaNGlX6mo4dO4aPj0+Z8oyMDPbs2UN0dDRFRUUUFhbSq1cvIiIimDRpElu3buW2227Dw8MDPz8/Dh48CEDr1q2xWq2YTCZKS0sdxvTw8Cgz/bSwsLDSfRcREREREec0wniN2bFjB+Hh4VgsFiwWC9OnT2fp0qV2dcaPH8+8efMoKSlh7969RnmrVq2cJliXNWjQgEaNGnHq1Cluu+02Nm7cCMDFixfZvn07/v7+lerv/Pnzueuuu6hfv36ZY+np6axfvx6LxUJWVhZfffWVMS31zjvvZNKkScTGxgLQrl07CgoK2L59u3H+hQsXKtUXERERERGpWhphrGGXp6QCBAYG4unpyT333GMc79OnD6NHj6ZPnz5Gmbu7O1FRUaSnp7N3716WL19O48aNadasGQsWLHAY5/KU1OLiYnr06EH37t2NmKGhodhsNh555BFatGhBQUGB3bljx47F09MTgA8//JDdu3cTEREBwM0338xbb71VJl5hYSGHDh0yHtgDcOutt5KdnU2vXr2IjY0lNjbW7kmwqampPPnkk5w4cQJ3d3dCQ0Np3LhxZW6niIiIiIhUISWMNWDr1q0AdOjQgaNHj5Zb18/PD4vFAmA3dTU5Odn4/Pzzz18xprPpmlOnTmXq1Kl2ZR06dDBipaamVritX/Py8jLWLF72zjvvGJ+joqLKJKZt27blk08+uWLbIiIiIiLiGkoYa5n8/HxiYmLsymbPnm339NHqsGjRImMtJVxah/jxxx9Xa0wREREREaleShhrGW9vb6xWq8vjDh8+nOHDh7s8roiIiIiIVB899EZEREREREQcUsIoIiIiIiIiDilhFBEREREREYeUMIqIiIiIiIhDbjabzVbTnRAREREREZFrj56SKrVGyZFfXB7TvfX1FK7NdGlMrz7h5D05yaUxAdq+8Qpnv/nWpTEb3d6Vc9/tdmlMgIadAyr0vtGq5OXlxckPF7s0JkDzPwzjxL8WuDRmi9EPArA3NMqlcf03fsqJ9xe6NGaLh0cCFXt/bVXy8vLizJdfuTRm46A7AMhfbnFpXO+B0S7/Tmz7xisA5Gescmlc70H3cXrdBpfGbBIRBtTMz3BNxISa+W469/0el8Zs2OlWAI7N+5dL4/o8Otql8aRqaEqqiIiIiIj8f/buOyyKq/0b+HcpahJjQUwUGxZEQBFREAFeDgAAIABJREFUEyzAomJLVCxgRDFojBUkRnwUE7sGsWPUGBNLYkGaYokNUMCGJhZEQewFQUUNoNLhvH/w7jy7sKzmyZwZfnJ/rmsvd2d2956zsjtzz5xzH0K0ooSREEIIIYQQQohWlDASQgghhBBCCNGKEkZCCCGEEEIIIVpRwkgIIYQQQgghRCtKGAkhhBBCCCGEaEUJIyGEEEIIIYQQrShhJIQQQgghhBCiFSWMhBBCCCGEEEK0ooSREEIIIYQQQohWlDBK5N69e6hfvz6USiWUSiUWLFgAX19fjefk5eVh0qRJUCqV6NKlC0JDQ4V1iYmJcHZ2hqOjIz777DM8fPgQALBt2zaYmZlBqVTC2dkZmZmZAICgoCDY2dnB3t4en3/+ufDcDz74AK9fvwYAnD9/HgqFAlevXsW9e/cwbNgwAICnpyf69esnxLazs6uwTQqFAidOnAAAFBQUoG7duli3bh0A4OHDhxgwYAAcHR3Ro0cPJCYmAgBiYmLQpEkT9OjRAw4ODli6dCmKi4sBAEqlEvb29sLn9OrVq3/xqRNCCCGEEEL+DUoYJeTo6IiYmBjExMTA0dGx3PpFixbB2toaMTExOHbsGAICApCSkoLCwkKMGzcOmzdvRmxsLGbOnImRI0cKr/Px8UFMTAycnJywc+dOAEBAQADi4uJw8uRJbN++XXiupaUlDh8+DAAICwtD586dtW5ramoqEhIS3timTp06Yc+ePQCAqKgomJmZCeu+/PJLLF68GLGxsdiwYQPc3d1RWFgIABg+fDiOHz+O48ePIyUlBZs2bRJed/jwYeFzqlmzpta4+fn5yM7O1rjl5+e/cXsJIYQQQgghb48Sxkrk2LFjmDBhAgDgww8/xMSJExESEoKzZ8+ia9euaNq0KQDAwcEBJSUlwlVGlczMTDDGAJRerTx37hyKi4tRt25d4TmDBg3C/v37AQBJSUmwtLTUui0zZszAsmXL3rjNzZo1w4MHD8AYw969ezF48GAAwIMHD1C7dm20b98eANCmTRu0b98e8fHxGq83MDDAnDlzEBYW9sZY6vz9/VG7dm2NW8CPa//RexBCCCGEEEJ0o4RRQrGxsUJXy6ysrHLrFQoF9PT++1/SpEkTpKWlIT09HY0bN9Z4rmodAAQGBsLKygqRkZHw8PAAAOzcuRNr1qxBq1atsGjRIuF1derUQW5uLs6ePYsOHTpUuK22trZ4/vw57t69+8Z2denSBXFxccjIyEDDhg0B4I3brM7ExERjeb9+/aBUKoWutNr4+fkhKytL4zbTe+obt5UQQgghhBDy9ihhlJB6l9TatWtrfU5JSYlwPzU1FSYmJjAxMcGjR480nqdaB5R2SU1ISICFhQVSU1MBAB07dkR4eDhSUlIQFxeHlJQU4bW9e/fG5MmTMXToUJ3bO336dKxYseKN7Ro6dCimTZsGpVIpLHvTNqtLS0vTWK7qknrw4MEKY1avXh21atXSuFWvXv2N20oIIYQQQgh5e5QwViK9e/fGr7/+CgB49eoVfv75Z7i5ucHOzg5nzpwRksFTp05BoVCgSZMmwmsNDAwwa9YsLFy4EABw8+ZNAEC1atVQp04djUR08ODB6Ny5M2xsbHRuj7OzMxISEvDs2TOdzzMzM0P37t2FojlA6dXErKwsodDNjRs3kJCQUK6ATlFREZYsWQJXV1edMQghhBBCCCHSM5B7A6qy0NBQXL58GQAwevRofP/99/jmm2+gVCpRUFAAX19fmJubAwB++eUXjBkzBgUFBahZsyZ27NhR7v2sra2RlpaG1NRU+Pr6IiMjA3p6erC3t4eFhQXOnTsHADA2NtYoMqOLt7c3vvjiizc+b+3a8uMHt27diilTpiA7Oxv6+vrYuXMnDA0NAQDBwcG4ePEiCgsL0b9/f3z99dfC6/r16wd9fX0AwO7du9GgQYO32lZCCCGEEEKIuChhlIipqalGYRelUon79++Xe97GjRu1vr5du3aIjIwst9zT01Pj8cmTJwEA+/bte+NzgdKpNlRU26e+bPjw4Rg+fLjWbSrbprIxmjZtigMHDpR7nVKpLFewRyUmJkbrckIIIYQQQoj0KGEkbyUlJUWo4KpCV/8IIYQQQgh5t1HCSN6Kubk5Xf0jhBBCCCGkiqGiN4QQQgghhBBCtKKEkRBCCCGEEEKIVpQwEkIIIYQQQgjRihJGQgghhBBCCCFaKRhjTO6NIIQQQgghhBBS+dAVRkIIIYQQQgghWtG0GuSdUZj2WPKYhiYN8DI6VtKYH/Z0ROrUmZLGBIDGawOQm3hN0pjvtbNC3rXrksYEgBpWbVCU8UzSmAb1jfFie7CkMQHAyGM4nm/eLmnMel95AABudu8jaVyzU0fxYtsuSWMaeboDAF6+fClp3A8//BA5f16UNOb7nW0BAFn7D0kat/bA/kj1miFpzMbrlgOQp62vYk5JGrOmsjsAef6G5YgJADft+0ka1+zkYeQlp0gas4aFOQDg2YbNksY1nvyVpPGIOOgKIyGEEEIIIYQQrShhJIQQQgghhBCiFSWMhBBCCCGEEEK0ooSREEIIIYQQQohWlDASQgghhBBCCNGKEkZCCCGEEEIIIVpRwkgIIYQQQgghRCtKGAkhhBBCCCGEaEUJIyGEEEIIIYQQrShhrCTu3buH+vXrQ6lUQqlUYsGCBfD19dV4Tl5eHiZNmgSlUokuXbogNDRUWJeYmAhnZ2c4Ojris88+w8OHDwEA27Ztg5mZGZRKJZydnZGZmQkACAoKgp2dHezt7fH5558Lz/3ggw/w+vVrAMD58+ehUChw9epV3Lt3D8OGDQMAeHp6ol+/fkJsOzs7nW3r2rUrfvjhBwDAy5cv0alTJ431Hh4eiI+PR15eHr755hsolUo4ODjA3d0d2dnZ//izJIQQQgghhIiDEsZKxNHRETExMYiJiYGjo2O59YsWLYK1tTViYmJw7NgxBAQEICUlBYWFhRg3bhw2b96M2NhYzJw5EyNHjhRe5+Pjg5iYGDg5OWHnzp0AgICAAMTFxeHkyZPYvn278FxLS0scPnwYABAWFobOnTtr3dbU1FQkJCS8sU0PHz5Es2bNEB0dDQD48MMP0bhxYyQnJwMACgoKcPnyZXz66adYvHgxzM3NERMTg7i4OPj5+aGoqEjr++bn5yM7O1vjlp+f/8btIYQQQgghhLw9Shj/Dzl27BgmTJgAoDTxmjhxIkJCQnD27Fl07doVTZs2BQA4ODigpKREuMqokpmZCcYYgNKrlefOnUNxcTHq1q0rPGfQoEHYv38/ACApKQmWlpZat2XGjBlYtmzZG7c5LCwMo0aNQsuWLXHr1i0AgKurK8LDwwEAkZGRcHZ2hkKhQGRkJCZOnCi8tl27djAyMtL6vv7+/qhdu7bGLWDdj2/cHkIIIYQQQsjbo4SxEomNjRW6pGZlZZVbr1AooKf33/+yJk2aIC0tDenp6WjcuLHGc1XrACAwMBBWVlaIjIyEh4cHAGDnzp1Ys2YNWrVqhUWLFgmvq1OnDnJzc3H27Fl06NChwm21tbXF8+fPcffuXZ1tio6ORu/evTFixAihC+2AAQNw8OBBAKUJpaurKwCgpKQECoUCAPDFF1/AxsYGx48f1/q+fn5+yMrK0rjN9PLWuS2EEEIIIYSQf4YSxkpEvUtq7dq1tT6npKREuJ+amgoTExOYmJjg0aNHGs9TrQNKu6QmJCTAwsICqampAICOHTsiPDwcKSkpiIuLQ0pKivDa3r17Y/LkyRg6dKjO7Z0+fTpWrFhR4frU1FRcuXIFAwYMgL+/v5Ak1qpVCx9//DGuX7+OixcvCmMg9fT0hCugu3fvhouLC3JycrS+d/Xq1VGrVi2NW/Xq1XVuLyGEEEIIIeSfoYTx/5DevXvj119/BQC8evUKP//8M9zc3GBnZ4czZ84IyeCpU6egUCjQpEkT4bUGBgaYNWsWFi5cCAC4efMmAKBatWqoU6eORiI6ePBgdO7cGTY2Njq3x9nZGQkJCXj27JnW9WFhYQgMDMSRI0dw7NgxtGnTRqNb6pQpU9CjRw/hqqKzszN++ukn4fUVjV8khBBCCCGESIMSxkosNDQUvXr1Qq9evfD777/j+++/x8WLF6FUKtG7d2/4+vrC3NwchoaG+OWXXzBmzBg4OjrC398fO3bsKPd+1tbWSEtLQ2pqKnx9fdG1a1d0794drVq1goWFhfA8Y2NjbNq06a220dvbG7dv39a6Ljw8XKN4T8+ePYVuqQMHDsTp06eF7qgA8P333yMlJQUODg7o06cP7t69W66iKiGEEEIIIUQ6BnJvACllamqKsLAw4bFSqcT9+/fLPW/jxo1aX9+uXTtERkaWW+7p6anx+OTJkwCAffv2vfG5QOlUGyqq7VNfNnz4cAwfPlzrNqliqbi7uwv3a9Wqhby8PI31NWrUQGBgoNb3IoQQQgghhEiPEkYiipSUFKGCq8ru3bvRoEEDmbaIEEIIIYQQ8m9RwkhEoZo/kRBCCCGEEPLuoDGMhBBCCCGEEEK0ooSREEIIIYQQQohWlDASQgghhBBCCNGKEkZCCCGEEEIIIVpRwkgIIYQQQgghRCsFY4zJvRGEEEIIIYQQQiofmlaDvDMKH6VLHtOwUUM8XbZW0pgf/WcqHs9fKmlMAGgwfxaervhR0pgf+Xoj+3CkpDEBoFY/ZxTcuSdpzGotTHG73zBJYwJAy8NhuNm9j6QxzU4dBQA837xd0rj1vvLA3UHuksZsvm8XAODxPH9J4zZY4IdH07+XNGajlYsBAFn7D0kat/bA/pL/JjaYPwuAPG2VY58DAC9fvpQ07ocffihLTECe36aCB6mSxqzWtDEA4O6w0ZLGbR72u6TxiDioSyohhBBCCCGEEK0oYSSEEEIIIYQQohUljIQQQgghhBBCtKKEkRBCCCGEEEKIVpQwEkIIIYQQQgjRihJGQgghhBBCCCFaUcJICCGEEEIIIUQrShgJIYQQQgghhGhFCSMhhBBCCCGEEK0oYSSEEEIIIYQQohUljJXAvXv3MGzYMOHx/PnzcfDgQbi7u+PPP/8UlkdHR2PixImIiYmBr68vAECpVGLSpEnCc+zs7IT7mzZtgoODAxwdHdG3b18kJiYK6/z8/NCnTx+Nbahfvz6USiXs7Oxw4cIFAMC2bdtgZmYGpVIJpVKJsLAw4TVdu3bFDz/8oLNtjDE0btwYu3btAgDcuHEDn3/+ucZzHB0dkZaWhszMTHh6ekKpVMLe3h4TJkwAY0zr++bn5yM7O1vjlp+fr3NbCCGEEEIIIf8MJYyVmJubm0aCFhYWBldX13LP++uvv/DkyRONZVFRUYiNjcXx48cRGxuLHTt2QF9fX1h//vx5vPfee3j+/LmwzNHRETExMVi1ahWWLl0qLPfx8UFMTAxiYmKExPbhw4do1qwZoqOjdbbh9OnT6N+/P/bu3QsAaN26NZ4+fYqsrCwAwOPHjwEAJiYm8Pb2hpubG2JiYnDy5EmMGjWqwoTR398ftWvX1rgFrPtR57YQQgghhBBC/hlKGCuxvn374tixYwCAkpISnDp1CkqlstzzvLy8EBgYqLFs165dmDVrFgwMDAAAxsbGsLS0BABcunQJtra2cHNzExI5dZmZmRUmaiphYWEYNWoUWrZsiVu3blX4vNDQUPj4+CA/Px8vX74EAAwaNAgHDhwAAISHh2Po0KEoLi7GrVu30L9/f+G19vb20NPT/ifq5+eHrKwsjdtML2+d20wIIYQQQgj5ZyhhrMRq1KgBCwsLXL58GSdPnkTXrl01rhKqDB48GNHR0UJCBgDp6ekwMTEBACxduhR2dnZC99HQ0FAMHz4cgwYNwh9//CG8JjY2Fp9++ilGjx6NuXPnCssDAwOFLqmnTp0CUNo9tnfv3hgxYgRCQ0O1bj9jDCkpKbCyssLgwYNx8OBBAICrqyvCw8MBAHv27MHQoUPx7NkzGBsbC69VKpVo27Ytbty4ofW9q1evjlq1amncqlev/uYPlRBCCCGEEPLWDOTeAKKbKrnKzMzU2h0VAPT09DBu3Dhs3LhRWGZiYoJHjx6hXr16mDVrFuzs7ISE7dChQ7h48SIAICkpSeiW6ujoiLCwMAQGBiI+Ph7W1tYASrukenl5Ce+dmpqKK1euYMCAASgpKcHr16/h5+dXbrvOnDmD27dvo2/fvigoKICxsTFGjBiB1q1bIz09HXfu3EFRUREaNWqE4uJiZGRkCK+NiYmBp6cnCgoK/uUnSAghhBBCCPlf0RXGSq5fv344cuQI4uLitHZHVRk9ejSCg4NRWFgIAHB3d0dAQACKiooAQPj38uXLcHR0xJEjR3DkyBEsWbIEERERGu81efJkbNy4UXivslRJ5ZEjR3Ds2DG0adNGa7fU0NBQhISE4MiRIzh+/DhycnLw6tUrAMDnn3+Or7/+GkOGDAEA6Ovrw8zMDIcOHRJer9pmQgghhBBCiDwoYawkTp48iV69eqFXr15Yvny5sLxGjRpo1aoVPv30U2E8ojbVq1eHq6urcLWwZ8+ecHBwgJOTE3r16oXVq1dj+PDhCA0NhZOTk/C6nj17lutSamhoiD59+gjdRtW7pG7btg3h4eFwdHTU+R6MMRw/fhzt27cXlnXt2lUYu+jq6ooTJ05g6NChwvq1a9ciODgYjo6O6NOnD2rWrAlTU9O3/QgJIYQQQgghIqMuqZWAqalpuSqn6oKCgjQeq5I3oLTrpsrMmTMxc+ZM4fGECRMwYcIEjdd27txZ47GJiQmOHDkCABoVWf39/YX7np6eGq8p+9jd3b3cNisUCly5ckVj2ezZs4X75ubmKCkp0Vhft25d/Pbbb+XeixBCCCGEECIPShiJKGJjYzFv3jyNZerJLCGEEEIIIeT/HkoYiShUczgSQgghhBBC3h00hpEQQgghhBBCiFaUMBJCCCGEEEII0YoSRkIIIYQQQgghWlHCSAghhBBCCCFEKwVjjMm9EYQQQgghhBBCKh+6wkiqtPz8fMyfPx/5+fnvfNyq1Fa54lJb38241NZ3My619d2MS219d+MS+dAVRlKlZWdno3bt2sjKykKtWrXe6bhVqa1yxaW2vptxqa3vZlxq67sZl9r67sYl8qErjIQQQgghhBBCtKKEkRBCCCGEEEKIVpQwEkIIIYQQQgjRSn/+/Pnz5d4IQuSkr68PpVIJAwODdz5uVWqrXHGpre9mXGrruxmX2vpuxqW2vrtxiTyo6A0hhBBCCCGEEK2oSyohhBBCCCGEEK0oYSSEEEIIIYQQohUljIQQQgghhBBCtKKEkRBCCCGEEEKIVlTaiBBCCCGSOXTokM71/fv3l2hL+Lt58ybMzMwAAPn5+ahevbqwLj4+HnZ2dnJtGvk/KCkpSed6S0tLibaEVDVUJZWQ/6+oqIhbeegNGzboXD958mQucadPn46VK1cCAAIDA+Hj4yOs8/T0xLZt20SPKdfBYFXZkebk5Ohc//7770u0JURsVSWRGjNmTIXrFAoFtmzZIuHW8GVra4uLFy+Wu6/tsZhiY2Ph6OgIALh79y6aN28urNuzZw+GDBnCJa4cCXJQUBBGjBgBADh9+jS6desmrFu3bh28vLxEjwnIs391cnKqcJ1CocDx48dFjwlUnd8mUjFKGEmV4ujoiNjYWACAh4cHtm/fLqzjufNesGCBzvXz5s3jEleOgxW5Dgbl2JHWr18fCoUCAKD6KVUoFGCMQaFQ4OnTp6LHbN68uRCjLIVCgTt37ogeU0WO9nbu3FmIqU4V8/z586LHBABXV1etcVVCQkJEjynXd0euz1gOUre1Q4cOuHTpUrn72h6LSa5EVY64VamtcqlKJ3mIdtQllVQp2dnZwv1r165prON57oRXQlgZbd26VZa4J06ckDxmRkaG5DHv3r0reUwVOdobFhYmeUwA3K5K6CLXd0eOzzgpKQm//vorUlJSAABt2rTB119/jTZt2nCNK3Vb1ZPTsomqrhMS/5b6/qzsvo3nvk6OuHK1VS4ZGRkICgrS+O64u7ujXr163GLK9dtEKg9KGEmVomsHzXPnDQBRUVHYsGGDxo/8lClT0KNHD24xc3NzkZycjJKSEuTl5SE5ORmMMTDGkJeXxy2uXAeDcuxIAeDKlStCTAsLC7Rt25ZrvMLCQhw+fFgjZp8+fWBoaMg1roqU7W3WrBkA4O+//8atW7cAAGZmZqhTpw63mACE7nzAfxPl+vXrc40JyPPdkfozjouLg5ubGyZOnIgJEyaAMYbLly+jZ8+eCA0NRdeuXbnEBaRv6927d+Hm5gbGmHAfKE1k7t27xyUmIF+iKkdcudoqx/41MTERvXr1Qv/+/dGhQwcwxnDx4kX4+/sjMjISVlZWXOIC8u3XSeVAXVJJldKkSRP4+fmBMYalS5fCz88PQOnOOyAgAA8ePOASd+/evZg+fTrmzp0LW1tbMMZw6dIlLF68GKtXr8aAAQO4xNXVTRPgc1VO/WBQ1dbLly9j06ZNXA8Gte1IL1++jKNHj3Lbkb548QIDBw5Eeno62rdvD8YYEhMT0ahRI+zbt4/LQeiDBw/g5OSEpk2barTz0aNHOH78OBo3bix6TBU52puXl4evvvoKf/zxB1q0aCEcaA8aNAg///yzxhgpMTHG8P333+Onn36CQqFASUkJ9PX14eXlhXnz5nE5EJXruyP1Z+zs7Iy5c+fC3t5eY/np06exYMECHDt2TNR46qRuq2oIREXUT0yI6YMPPoCVlRUYY0hKShJ+/xhjSE5OxqtXr7jErVu3LpydncEYQ1RUFJydnYW40dHRePHihegxDQwMYGRkBMYYMjMzUbduXSFmZmYmCgsLRY8JyLN/dXFxgaenJ1xcXDSW79u3D1u3bkVERIToMQH5fptI5UEJI6lS5BpL2L17d2zcuLHclZhr165h4sSJOHnyJJe4cpDrYFCOHemkSZNQp04dLFmyBHp6pbMUMcbw3XffITMz843Fjv4XI0eORNeuXTFlyhSN5T/99BNOnz6NHTt2iB5TRY72zpo1C+np6Vi/fj1q1qwJAHj16hW8vb3RoEED+Pv7ix4TAAICAnDixAls3LgRpqamAIB79+5h8uTJ6NGjB3x9fUWPKdd3R+rP2NzcXLhK8U/WiUGuvydA2ivV9+/f17ledaVVbHIlyFWFru9HmzZtcP36dS5x5TzJQyoHShgJkYCuH3ILCwskJydziy11N025Dgbl2JFaWFjg2rVrQvKkUlxcDCsrKy4x5TzYlqO9VlZWuHDhAmrUqKGxPDc3F506dSo3Flks7du3R0xMjHC1QuXFixdwcnJCQkKC6DHl+r+V+jPu2LEjLly48I/XiUHqtpaUlGDOnDmSXqlWJ3VX7sqmadOm3HoOAdLvX3UV0+FZaEfO/Q6pHGgMI6lS5s+fjyZNmuCrr77SWL5u3Tr8/fffmDNnDpe4H3zwQYXreE6DIMd4B9VZ+3+67t+S4zOuUaNGueQJAPT19csdkIpFVzt1rRODHO01MDDQ+t7vvfcet2lwgNID/bLJIgAYGRmhpKSES0y5vjtSf8Z37twRxvKpU43z40nqti5fvhwXLlzAxYsXy12pXrlyJZcr1YB8XbmdnJwqTIIVCgWio6O5xK0Iz2sicuxfHz16hP/85z/lljPGkJaWJno8Fbl+m0jlQQkjqVJCQ0ORmJhYbvmkSZNgY2PDLWG8fv06Pvnkk3LLGWNcz8zNmTMHP//8s9Zumt999x2XbppyHQzKsSPNzs7WOj8VY4zbGKGMjAytXT8ZY3j27BmXmCpytLegoEAoJlE2Jq+xSUBpEpybm6s1rr6+PpeYcn13pP6Mdf3u8K5OK3Vbd+3aVe5KtampKXbs2AEnJyduCeP8+fNhYGCA1NTUcl1v58+fz63r7bp168oti46OxooVK4SEWUo8r+DKsX9dtmxZhesCAgJEj6ci50keUjlQl1RSpVhaWlY4wbuudf+WXONJ5OimKdcYlt9++03n+i+//FL0mLrmpgL4lCKXaxwuIE97lUqlzoM+XtOpmJqaQk9PTyOxUJ9zksd8l3J9d6T+jF+/fl3h1fAHDx6gadOmosZTJ3Vb27Vrp/Uk5ZvW/VtydeVWt2/fPvj7+8PIyAh+fn7lxr+JZcaMGRXOrblp0yZkZWVxiSvXeMKKFBUVcet1QWNTCV1hJFVKrVq1cOXKFVhbW2ssT0hIwIcffsgtbkFBAczMzAAA+fn5Gt2B4uPjuSWMcnTT7NSpk86DQV50JYRFRUVcYsoxN5Wcc3rK0d6YmBjJYwLgOuVBReT67kj9Gdvb2wtjrXr27KnRTdHFxYXrhOdSt1WOK9WAvF25g4KCsHz5cpibm2Pjxo2wsbHhFg+Azml91q5dyy2uHPtXR0dHIXnz8PDA9u3bhXWffPIJt++OXL9NpPIoPxiFkHfY8uXLMXDgQCxatAgHDhzAwYMHsXDhQri4uGD58uXc4g4fPly436VLF411kydP5hZX1U2z7G3GjBncummqn0Xu2bOnxrqyXXfEpH6G08PDQ2Odtu7AYpg+fbpwPzAwUGOdp6cnl5gjRowQ7s+cOVNjXe/evbnEVJGjvStXrhTuh4aGaqybPXs2l5gAEBQUJNw/ffq0xjpt3e7EINd3R+rPWD15KjvNAu9OT1K3NTMzE1ZWVuVubdu2RXZ2tujxVFRdb5OSkjRu165d49qVu1WrVpg9ezY8PT3x5ZdfIi0tDYcOHRJuPIwaNQqurq748ssvNW6urq4YNWoUl5iAPPtX9b+ZsleJeX535PptIpUHJYykSrG3t8eZM2dQUFCAbdu2YcuWLSgoKMDp06fh4ODALa76D7m2M828LFu2rMKDFV7jHeQ6GJRjR6refa1sl9grV65wianezSkyMlJjnapsPy9ytHfnzp3C/bLjro4cOcIlJgCNE0je3t4a67Zs2cIlplzfHak/Y7kmWgfpZypcAAAgAElEQVSkb+u9e/dw584d3L17t9yNR7dmlQYNGmDy5MmYMmWKxs3Lywsff/wxt7gODg7o0aMHEhISEBoaqnELCwvjEtPHxwf79u0rt3z37t2YNm0al5iAPPtXXd8Pnt8dOU/ykMqBuqSSKsfExASLFi2SNKZcB0jq3TRVRUl4VzSTq61y7UilVlXaqSLXyRY54sr13ZG6raorM4wxjWJVvCs9qmJou6/tsVjy8vKwc+dOYfokCwsLjBw5kltlYYDf2N432bZtm+QxT5w4ofWq/5gxY9CuXTtuceXYv6qKnjHGNAqg8S56JudJHlI5UMJIqpTOnTtXODheoVDg/PnzXOLevXsXbm5uQkUxVbUxValzntasWYMVK1YgLy8PjDG8//77mD59Onx8fLj80Mt1MCjHjjQ3NxfJyckoKSlBXl6eUH2RMYa8vDwuMbOysnD48GGUlJQgKytL6ObFGOPaxQ2Qp72MMeTm5qKkpES4rzqw55kwynGAJNd3R+rPWL3SY9mqjzwrPQLSt/Xy5csYMmQIlEqlMJbv1KlTWLx4Mfbu3ct1fF96ejo2bNigkahOnjwZDRs25BYTAC5duoRVq1ZpxJ02bRpsbW25xKtojLpCoeA2fl1F6v3ruHHjhJ4k6vcBlJsuTExynuQhlQNVSSVVilzVSuWqMPb9998jKSkJq1evFtp27949fPvtt7CwsMCSJUtEjylHtVJAnuqhclTwlKNSqYoc7ZWjWilQWjTEyMgIjDFkZmYK0yIwxpCVlYWCggLRY8r13ZHrM5aD1G3t3r07li5diu7du2ssP3XqFGbOnFlufKxYjh8/jjFjxmDs2LHo0KEDgNJEbsuWLdiyZUu5cWhiCQ4Oxrx58+Dn5yfEVc1NuGDBAnzxxReix+zVqxdmz56NHj16aCw/ceIEFi1ahOPHj4seE5Bn/yoXuX6bSOVBCSMhKK3sFh4eDldXV7k3RVStW7dGUlJSuap4hYWFsLS0xM2bN2XaMkJIVTV27Fid63mNEZWDrumaLCwshKtwYrO1tcXOnTthYWGhsTw5ORnu7u64dOkSl7hWVlaIiooqdxUzLS0Nzs7OXKbzuHr1KlxcXODo6AgbGxsoFApcvHgRcXFxiIiI0FlF9d+QY/+6cOFCnevnzp0rekxCAOqSSqqY7OxsrF+/Hqmpqfj888/Rp08frF+/HitXroS1tTW3hLHsmc+yeJ0BZYxpLaFuaGjIJR4g38GgHDvSuLg4net5FFL6/fffda4fPXq06DFV5Gjvm0q285yzT2pyfXek/oz37NkDU1NTuLm5wcbGRtKiGVK3VaFQ4MmTJ+UKzaSnp3Md+5WXl1cuWQRKk9T8/HxucYuLi7V2eTUxMUFxcTGXmG3btkViYiJ27dqFpKQkMMbQtWtXrF+/Hu+99x6XmIA8+9eFCxeibdu2GDJkCIyNjSX77lSlkzxEO0oYSZXi4eGB2rVro1u3bti8eTOWLVuGoqIihIeHo2PHjtzipqeno1q1ahg+fDj69u2rMQ8jT7a2tli1ahW+/fZbjeUrVqzgNnZGroNBOXakSqUS1tbWaN26NQCU6+bGI4Hy9PSEtbU1evToAQMDg3IxeZKjvaampmjdujVq1aqlNSavccd6enqoV6+e8JmWjfv06VPRY8r13ZH6M87IyMDRo0cREhKCiIgI9O3bF66urlwLlKhI3dY5c+ZAqVRi9uzZQhfNCxcuwN/fn+ucqjVq1EBKSgrMzc01licnJ3Pd/9SrVw9nzpxB165dNZafPn0aRkZG3OK+99575cbwlZSUIDQ0lNuJYDn2r6mpqQgLC8PevXtRrVo1uLq6YvDgwUKXeV7kPMlDKgfqkkqqlLZt2+Lq1asASs+ENmjQAA8fPuRarU7l6tWrCAkJwdGjR2Fubg43Nzf06dOH69nIFy9eYMyYMUhKSkL79u0BlBZhsLS0xNatW1GvXj3RYxYWFgoHg9evX5fsYPDx48eS70iDg4MRFhaGtLQ0DBgwAG5ubmjRogW3eABw7tw5hIaGIiYmBp06dYKbmxucnJwkqVQnR3sDAgJw4MABfPTRR3Bzc8OAAQN0TpgtlkmTJuHPP/+Evb09hg8fDjs7O+4x5fruyPUZA6XzBe7atQu+vr6YO3cupk6dyjWeHG3966+/sGbNGqFrqqoITKdOnbjFjI6OxtixYzFu3DiNRJX3GEZVkR8nJyeNuDExMdizZ4+wTEzqPYcGDBiA3r17Y8OGDVixYgWsra2xf/9+0WMC8uxf1aWnp2P37t0ICAhAQEAA13GEcv02kcqDEkZSpdja2uLixYsVPpbKjh074OPjg1mzZmHGjBnc4926dUvjYMXMzIx7TED6g0EVKXekAPD69WscOHAAYWFhePz4MRYtWgQnJyeuMQEgPj4eoaGhOHToEPz9/SWbQFmO9t65cwchISE4ePAgGjZsCD8/P25VF1VKSkoQExODkJAQ/PXXX1AqlZg0aRJatmzJNS4gz3dHys84KysLe/bsQVhYGHJycuDi4gJ3d3fUr1+fS7yy5Ph7ktqjR4/w008/afz2T548GY0aNeIaNycnR+geqoo7cuRIvP/++1ziDRo0SOg5FBkZiefPn6OoqAhr1qzh2nNIRer9K2MMsbGxCA4Oxvnz5+Hg4IApU6agVatWXOOqyLVfJ/KiLqmkSrly5Qo++ugjoTtFZmam8JhXNzOVpKQkBAcH4+jRozAzM8OWLVvQr18/bvGA/47XqVatmkYXGdVyXuO/yh4MzpkzByNGjOASS13ZHemIESPQrVs37nH19PSEK3wlJSXc4wGl1fhiY2Nx7tw5WFhYSDqWT472tmjRAk5OTsjIyMDBgwfx4MED7gf4enp66NGjBywsLLB7924sXboU7dq145owyvXdAaT7jAcMGIDHjx/DxcUFq1evRuPGjYV1OTk53BILdVK1Vc6xX40aNcLixYu5vX9F3n//fYwbN06yeLdv3xZ6Do0bN06ynkNy7F+9vLwQHx8Pe3t7jB49Gj/99JPoMSoi528TkR9dYSREAm3btoW+vj7c3NzQr1+/cjsyS0tLLnH19PQkH/+lfjDo6uqqcTAIgNvBoPqO1M3NDV26dOESR11YWBhCQ0OFIkpubm7crz6tWLEC+/fvh7GxMVxdXTFw4EDJug/K0d6//voLISEhOHHiBGxtbeHm5galUgl9fX2ucZ8+fYqwsDDs2bMH1apVw9ChQ+Hi4sK1m5lc3x2pP2NTU1PhhIN6V2oppvGQuq116tTROfbrs88+4xJXrkJrzZs31znXMY//W7l6Dsmxf9XT04ORkVG57w/vk95y/TaRyoMSRlKl5OXlYePGjbh16xasra0xduxYrVXOxKY+f13ZIhoKhYLbzluO8TpyHQzKsSPV09NDu3bthCIwZQ+UQkJCuMQ0MzMTDlLKxuRVBEYVW472tmvXTijyUzZm2YnfxWJgYAArKyuhiFLZuJMnTxY9ppzfHTk+4ze5fv062rRpI+p7St1WucZ+WVhY6Cy0ZmVlxSXu69evAZT+zXbr1g1nzpzRWM9j/6OaM1UVVzVvKu8kSs6xv2+SnZ0t7CPEIOdJHlI5UMJIqpThw4fD0NAQ9vb2OHz4MJo1a4bAwEC5N0tw/PjxN54Z/l9UxvE6PA4G34aYO9LY2Fid6x0dHUWJo+7+/fs616smkOZBjvbKNWH0/PnzdRYS4lnh8k3E/u5U1km5eVwpkrOtUo/9kqPQmjq5agRIrTLuX+X67OXarxMJMEKqkLZt2wr3CwsLWYcOHWTcmvJ4bk98fDz79ttvWevWrdnevXu5xXlbcn32csR1dXWVPGa3bt0kj6kiR3t9fHwkj8kYYwEBAZLHlOu7I/VnbGNjI2k8dWK2NTMzk23ZsoX179+fKZVKtmbNGvb06VPR3v9tbN++nRkZGbFly5ZJFlOqv9PXr1/rvEmhsu1f5fruVLZjKiIeKnpDqhT1M6tSdEX9p5jIF/y1jddZtmwZ9/Ffb0PstlbmuDdv3pQ8pqprmBzkaG9cXJzkMQFg9+7d+M9//iNpTLm+O1J/xlJMFVMRsdoqZ4EfOQqtbdiwAUDp32hGRobwWIVHV24rKysoFAqt3wue3SUr8/5Vru+OXL9NhL/Kd8RMCEeqKqnAf8c6SFUl9W2I/SP/ySefaIzXOXr0KI4ePSqsl2tsEiDfDk2OuFUlZmWILTU5DpCq0uf7f11iYiIUCgV+/fVXbN68WVjOOI/9Ui+0tmHDBqHQ2q1btwDwK7SWkZEh3B83bpzGY17u3r3LPYY2lXn/Khf6bXp3UcJIqpSioqK3ep7YA8bfltgHn1u3bhX1/QghmugAiR9TU1O5N+Ffu3fv3ls9T+yxX6pCTVFRUYiOjgYgTaE11djeZ8+ewdjYmEuMsuLi4pCVlYUBAwZoLA8PD8dHH30Ee3t7LnEr8/61WrVqcm8CecdQwkiIFkqlUrIB4zdu3BCqToo9Z+DbFnD45ptvsGbNGlFja/Py5Ut8+OGHAOQ7GJRjRyrHVSg5uwZVpfbKEVeu746Ybc3IyEBQUBBSUlIAAG3atMGIESM0kow9e/aIFu+fkvr/1d3dXdR9TkxMzFs9T+xCawcPHsSYMWNgaGgIPT09hISEoGvXrqK9vzazZs3C3r17yy3v0qUL3NzccOrUKS5x5di/quZ4rIhq7sf4+HhR4v1T78JJHqKdntwbQEhlxPtg4fbt2/D394eNjQ2GDRsmLF+3bh3XuBXhOTbp9evXCAoKgouLC8zNzYXlUhwMnj17Ft98841G5VCeO9Jnz55hz549uHDhgsby7du3ixYjJycHhYWFwuPk5GSsWrUK4eHhGs9TXVXgraCgABEREXB3dxeWidnetxUQEMA9xosXL8r9Nvj4+Ij2/ufPn8fjx4+Fx1u3bsXAgQPh7e2N58+fC8ulSqQSEhIwe/Zs4bFYn3FiYiLatm2LS5cuwdzcHK1bt8bFixdhbW2NpKQkUWJURFcXyUuXLgn3pfh7UifXCQ9fX19R32/27Nk4efIk0tLSEB4eDj8/P1HfX5tXr17h448/LrfcxMQEL1++5B7/TcTcvw4dOhTDhg3D0KFDhduwYcPQpUsXNG/eXLQ4ZVW23yYiAwkL7BDyfwaPSl/37t1jAQEBrGPHjszGxoYZGRmxGzduiB7nfyF2e3NyclhISAgbMmQIa9KkCatVqxaLjIxkhYWFosbR5vz58+zbb79lzZs3Z++//z775Zdf2JMnT7jE6t+/P7t69SpjjLGHDx+yjz76iH322WfM0tKSrVixgkvMbt26sZs3bzLGGEtOTma1a9dmU6ZMYb169WK+vr5cYpZVUFDADhw4wEaNGsWMjIzYyJEjWWhoKJdYSqWSOTk5ab316NGDS0zGGJs/fz5LTk5mjJX+PTs4OLC6deuy+vXrsyNHjnCJaWNjw168eMEYYywqKoo1aNCAhYaGsnnz5rFBgwZxiVlWYmIi+/7775m5uTnr1q0bl7/jQYMGaa0kGRERwb2dLVq0YGvXrmXFxcXCsufPn7Px48fLWuFRrthiV9Ms2w4p2mVmZsby8vLKLc/JyWGtWrXiHv9NeH4G165dYx4eHszCwoL98ssv3OJUht8mIi9KGAnRQuwf+E8++YS1a9eO/fDDD0KSaGpqKmqMf0PM9g4fPpw1a9aMjR8/nh07dowVFhZK0taZM2cyMzMz1rt3b/bLL7+wjIwM7nEtLS2F+wsWLGAeHh6MMcZevXqlMYWLmNTfd9asWWzKlCmMsdIkzsrKiktMlcOHDzNPT0/WpEkTNnLkSBYREcGaNm3KNebVq1fL3QIDA1mTJk2Yvb09t7gWFhaspKSEMcbY+vXrmaOjIyssLGTXr19nHTt25BLT2tpauD9+/Hg2f/584XH79u25xGSMsaSkJDZ//nxmaWnJunXrxtasWcOaNGnCLV7r1q0rXGdubs4tLmOMPXv2jE2YMIHZ2NiwEydOsB9//JG1aNGC/fjjj6yoqIhrbF3elWmGGjduzNavXy/cyj7mYeHChWzgwIHs4cOHwrIHDx4wFxcXtmDBAi4x/wke/7fnz59nLi4uzNbWlgUHB2ucAOFBrt8mUnnQGEZCtBB7nJuZmRlOnjyJGzdu4M6dO2jRokWlKpbBROwOlZqaivfffx8mJiZo3LgxDAwMJGlrUFAQGjZsCHd3dwwePBi1atXiHld9apaoqChhIu4PPviAW2l19TZFRUUJRSYMDQ25l3Pv378/unXrhmPHjgkFOqZNm8Y1ppWVlXB/37598Pf3h5GREXbu3MmtmAUAVK9eXfisjx49ipEjR8LAwADm5uYoLi7mFjc/Px/VqlXDsWPHsGvXLmG5ejdksVlZWaFbt26IiIiAmZkZACAwMJBbvA8++KDCdTynmQCAevXqYePGjVi1ahV69OiBhg0b4ty5cxrTXcjhXRiXCpSvjCpFpdQ5c+YgMDAQXbp0QW5uLhQKBWrUqIEZM2bA29uba+y3IeZnfPz4cfj7+yM/Px9+fn7cp0lRJ8dvE6k8KGEkVcr9+/dRp04d1K5dG0DpAff+/fvRrFkzeHl5oXr16gDEH+e2Y8cO5Ofn4/Dhw/jtt98wfvx4/P333zh48CCcnZ2FuGLLyMhA/fr1ta67dOkSOnToAEDc8TqnTp1CWloaQkJC8NVXX+H169fIzMzE1atX0bZtW9HilHX//n2cPXsWwcHBWLRoESwtLfHq1StkZmaiTp06XGJaWFhg/vz5MDY2RnJyMvr27QsAyMrKQklJCZeY3bt3x5gxY2BsbIwnT57A2dkZAPDkyRPuc4veunULwcHBGDFiBKpXrw43N7e3rjz8vyopKUFQUBCWL18Oc3NzbNy4ETY2NlxjAkCtWrUQGxuLevXqITY2VphPrri4GLm5uVxienl5oWPHjqhZsyZatmyJLl26ACgdS6iaDoiHY8eOITQ0FM7OzrCzs4Obmxu3v18AePTokda5KxljSEtL4xYXANLT0/Htt9/i4cOHOH36NI4cOYLevXtj8eLFGDJkCJeYla3AD89Ca6oTWG+ybNkyUecv9fHxgY+PjzBmUVVcTd2OHTswatQo0WK+LTH3r7169ULjxo3x6aefYuvWreUqtYaEhIgWS51cv02k8lAwsU8vEVKJffLJJ4iIiICJiQn++usv9O7dG7Nnz8a1a9fAGMO2bdsk2Y68vDz88ccfCA4ORmxsLJ48ecIlTsuWLfHNN99gypQp0NMrrXH14sUL+Pn54c8//5SkEuzDhw8REhKC4OBgvHjxQpgHjLeTJ08iJCQEERERaN26NZciMK9fv8batWvx+PFjeHp6Cgn4mTNncPv2bXh4eIges6SkBMHBwXj8+DGGDRuGJk2aACg9AfD06VP06dNH9Jja3LhxA8HBwQgJCUG1atXg4uKCOXPmiB6nRYsWKC4uxrRp04SDXHX9+/cXPSZQOs2Bj48Pnjx5Am9vb3z11VcAgMjISBw+fBirVq3iEvfRo0d4+vQp2rdvL3xn09PTUVhYKFRA5KW4uBjR0dEIDg7GgQMHYG9vDxcXF9H/jn/77Ted69+2+uT/wszMDHPmzMHo0aOFZXfv3sW0adPw6tUrREVFiRovMTERvXr1Qv/+/dGhQwcwxnD58mUcPXoUUVFR3OZDLOv27dvC73BJSQmuXLkiSdyK2NraSlaJnFdMJyenCnuxKBQKLvuc2NhYnesdHR1Fj6ki528TkR8ljKRKsba2FnaU33zzDWrUqIGlS5eCMQZra2skJiZyifv69esKu2ElJSVxO2h4/vw5vvvuO5w7dw6rV6/G1atXsXr1akybNg2TJk3i3oWxLPUz21JhjCE2NhZKpVL09x4xYgSCgoJEf19dHB0d33jQILWkpCSEhoa+9dWFf8LT01PnQdmWLVtEjwkAK1euxPTp07m8d0WCgoIwYsQIAMDp06c1rv6sW7cOXl5ekm1LUVERjh49irCwMEnnm/P19cWKFSu4vb+uOXYjIyOFK/ZicXFxgaenJ1xcXDSW79u3D1u3bkVERISo8dTdv39fOKlTXFyMBw8eID4+Xuh2LKcOHTpoVKX9vxjz2rVr5ZZFR0djxYoVMDU15Vp9PDc3F7dv3wZQemL4vffe4xZLl5s3b2LNmjVYv369LPGJhOQZOkmIPNq2bSsUsrC0tGRRUVEa63hRH/RetrKjFMUOVq5cyRQKBTMxMdEoDMCDg4ODcH/UqFEa63i29YsvvhDu/+c//9FY5+zszCWmHIUqxK5q+E+oV8wMCQnRWOfn5yf15nAlx/+tekwpq03u2rVLuH/q1CmNdT/++CO3uNrwLLZTkTNnzjAfHx8uBZzkKvBTlQqtVYaYERER7NNPP2X9+vVjcXFx3OLk5+ezqVOnsnr16jFbW1vWoUMHZmxszHx8fLRWihVLQkICc3Z2ZpaWlmzWrFksLS2NDRkyhDVs2JCtXLmSW1xSedAYRlKluLm5wdnZGXXr1oW+vj6cnJwAAHfu3NE65kEsTO1C/osXLypcJzY5xutkZ2cL98uegeXZ1uvXrwv3IyMjNcaN8Cq6kJWVhUOHDlW4nkd3yWfPngnj6bSZPHmy6DFVdu7cKVx18/f3h6urq7DuyJEj+OGHH0SPqautAL/2lpSUIDc3t8K/WR7FWdRjlY3L87uzfPly4cqmt7e3Rre9LVu2SHplUyp//vkndu/ejb179+LJkycIDAzUmHNSLHIV+KlKhdbkiinH+OqpU6fC0NAQ9+/fF/62Xr16BT8/P3h7e2PTpk1c4n799dcYP348unfvjj/++AM2Njbw8PDAjh07ZLu6SaRFCSOpUubMmYNevXrh8ePHcHZ2FvrhFxUVYd26ddziqu+oy+60ee7EHRwcNMbrdOnSBZ6enpg2bRo2bNgg+ngdQHd7eLZVjriZmZkICwvTeiCiUCi4JIxFRUV49uyZ7AdcUiU0vCssViQ5ORlWVlYa7VIoFGCMQaFQ4M6dO6LHlOt3Qur/16SkpAq3g3cRpVmzZmHPnj1o3rw5XF1d4efnh86dO2PcuHFc4slV4EeuQmtA6Xf2/v37aNWqVYUFx3x8fLjFV7dmzRp88803AKBxgksMrVq10hhfnZaWpvF/yuP3Pzo6Gjdv3tRYVrNmTaxdu5brcI/8/HxhHLe5uTnWrVvHtes4qXwoYSRVTpcuXXDlyhUcPnwYQGnFunbt2nGNqTpoYIxpHEDwPmi4cOFCufE6zZs3R0REBCIjI7nEzMjIwIYNG8AYE+4DpW199uwZl5hA6dW+w4cPo6SkROPKH2NM46qnmJo1a8ZtDF1FGjZsiLlz50oaU4UxhtzcXJSUlAj3VQkFr4SRx7jIt2FpaSn5GKsrV67go48+AmMMmZmZQvVBxhiysrK4xZU6UZ0yZUqF68zNzUWPp07q6XeWLVtW4Toxq2dqU716dbi4uMDFxUUotPb777/jq6++4lZo7ddff8Xs2bPRsmVL3L17F5s2bcLAgQPLPc/T05NL/LJWrVolJIxiX0F2cHCAQqFAQkICEhISNNbxOmFY0e+s6mQWL/n5+UhOThZi1KhRQ+OxVMWbiHyo6A2pUl68eIGBAwfi8ePHsLa2BmMMiYmJaNSoEfbt28dt+gU5qwKWpZp6Yu/evbh//77o779gwQKd63klAGPGjNG5nkfhjnehcMM/YWpqCj09Pa1X3YDSapNiW7hwoc71vJJnOT9nqRkYGMDIyEhIVOvWrQvgv4lqQUGBzFsoLtVv4MGDB2FpaYn4+HjcuHGD2+9/RXgW+JGr0JqVlRViYmJQv3593LlzByNHjsTZs2e5xHobTZo0wcOHD2WLL7aJEyeiWrVqCAgIELqC5uTkYObMmcjPz+fWJVU1fEcbhUKB48ePc4lLKg9KGEmVMmnSJNSpUwdLliwRuqMyxvDdd98hMzPzjeOleEhLS4OJiQnXGNrG6wwcOFDy+ZPy8vJQo0YNSWPy9OjRIzRq1EjSmKoukVWFgYEB2rZtiyFDhsDY2LjcWXRdV6v+jYiIiHKVLXlLTExERkYGevToobE8MjISJiYmsLKyknR7eNE17hfgN1WKNidPnkRwcDD27dvHbfqdijRt2hQPHjzg8t7qU0j07NlTo108p7Qo+94dO3bEhQsXuMRSycnJ0bqcMYY2bdpwSxjlGF+dn5+PGTNmYNeuXTA1NQVjDA8ePMCIESOwbNmyd2r/SioXShhJlWJhYYFr164JyaJKcXExrKysNAqniO3s2bNITU2Fvb09GjRogCtXrmDZsmWIi4vjdtBQdryOi4sLOnfuzOVKkLpHjx4hLS0N7du3R7Vq1fD06VOsXbsWv/32G9ezvcXFxXjx4gXq168PACgoKMD27duxZs0aLlOmNG/eXCN5U0/mFAqFUPZcTPXr1xdiqH6+FQoF8vPz8fr1a+5jwLThWVr98ePHCAsLw969e1GtWjW4urpi8ODBwlUwXsaMGaNzOo/NmzeLHrNXr174+eef0bJlS43l169fh7e3N7du5BXJysrCpk2bMGPGDFHfV1dvAJ5TpejCOE6/UxGeCaP6FfKyV8t5Xj3/+OOPhR4zjDH8/vvvGj1odHXR/V+pfocrOpzltb+TqzcNUHoFWX1aDV3FlcSSnp6ODRs2IDk5GUDp8dTkyZPRsGFD7rGJ/GgMI6lSatSoUS5ZBAB9fX2uZ+ZmzJiB/fv3w9bWFv7+/ujfvz82bdqEWbNm4ZdffuEWV+rxOkBpkYHFixfDzMwMOTk58Pb2xowZM+Dh4YH4+HhucXfv3o3x48ejZs2aaN68OebNm4exY8fC1taW2zxyV69e1XisnqB26dKFS8yyRWCeP3+OwMBA7NixA97e3lxiqly5cgW+vr549OgRBg4ciKlTp8LLywtnz56Fr68vl5gNGjSAl5cXvLy8kJ6ejt27d8PCwgIBAQFcu3IPGzas3LIbN/LN+WYAACAASURBVG5g9erVMDDgs+t8+vRpuWQRKB1n/fTpUy4xAeDhw4dYtGiR8P86atQozJ07F7/99hvc3d1FjyflvI5lvamLs9gJo1wFfuQqoFQ2IeSRIJbF+wRoReQYX63t6nxqaqpwn9fV+ePHj2PMmDEYO3asUETv0qVLsLOzw5YtW9CzZ08ucUnlQQkjqVKys7O1/uAyxvDq1Stucf/44w9cvnwZ7733Hl68eIEmTZrg2rVrMDU15RYTKJ24WTVeZ9GiRbC0tMSrV6+QmZnJbbzOzz//jOvXr8PY2Bj37t2Dubk5Tp8+jU6dOnGJp7Jo0SL89ddfaN26Nf78809069YNYWFhWgsuiEV1Vjc3NxebNm3C+vXr0atXL0RHR6N58+bc4gKlV3FXrFiBAwcOYPz48UhISOA6NQwgX2l11dWf4OBgnD9/HiNGjNCY1J6Hzz77TLifkJCAH374AcnJyViyZAmXJAoo7VqnrctxSUkJ19+n0aNHo3v37hg6dCgOHTqEzp07w9raGleuXOHSXb5s1VCFQgFjY2PY29vDzs5O9HjqFi5cqLOLs9jkKvAjV6E1Kcfj68J7rD4gz/jq0NDQCtfxKrQDlI63PXLkCCwsLIRlAwcOhJubG9zd3avMeO+qjLqkkipFjsIoQPlxHXIV1JBivE7ZtqoOPHkrG9fKyqrcPJBiy8rKwrp167BlyxYMGzYM3377LT7++GOuMW/duoWAgACcOnUKU6dOxdixY7mWyFdnY2ODy5cvC49btGjBZXoJdV5eXoiPj4e9vT3c3Ny4XbnV5vTp01iyZAn+/vtvzJo1C4MGDeIab9q0acjNzcXq1auFBDw3Nxe+vr7Q19fH2rVrucRt3769RpXHRo0a4eHDh1p7Y4hBWxGwFy9eICwsDMOGDcO0adO4xAXk6+IsNbkKrY0dO1bnep7djaUeqy/H+OqgoCBhzlQpWVpaVni1XNc68u6ghJEQCdStWxfOzs4ASs/wRkVFCY8BICQkRNLt4TleR44xLEBpNTw/Pz/hsb+/v8ZjHgUIateujTp16uDrr7+GkZFRufU8YhoYGKBRo0YYMmQIDA0Ny63n2QXMwsICe/bsEQ6Mhg0bhvDwcK6l1fX09GBkZKQxNhT473hRXl01HRwccP/+fUyfPh29evUqt55HWwsKCjBz5kxs374dzZo1AwA8ePAAHh4e8Pf353ZiwMbGBmfPnhX+H7t27arxmOcE8+ry8vJgZ2encVKCJ1UX54CAAG5dnCtTgR8VnoXW6tSpA1NTU7i5ucHGxqZcEqV+5V4sco3Vl+PkA8+CRW+KGxQUVO6qeHJyMl1hrCIoYSRVzqVLl7Bq1SqNgdvTpk2Dra0tt5ixsbE61zs6OnKJK0eXGV1nthUKhTD+QWy6ChAoFAoubZ0/f77O8UA8xrhs27ZNZ0yeXcKqUml1pVKps+gNz7bm5OTg9u3bYIyhVatW3BM2bdOlqCgUCu5XkdVJ0fuibBdnBwcHTJkyBa1atRI9lpwFfuQotFZYWIijR48iJCQE169fR9++feHq6sp1ruNmzZqhYcOGmDRpkjBWX4reD+qkOPkAyJcwRkdHY+zYsRg3bhw6dOgAoHSe5y1bttAYxiqCEkZSpQQHB2PevHnw8/MTfvQuXrwIf39/LFiwAF988YWk25OQkIDg4GD88MMPXN5frikJtHn+/DnCw8Mxfvx4yWICpeO/oqKi0Lt3b0njyqGoqIhbQZY3efnyJZcxlDk5OTA0NBSupiYnJ+Pw4cMwNTXFkCFDRI8np7i4OJ3rHRwcJNqS/+IxjYu2aRD+/vtv/P7770hISMDu3btFjadOzi7OUlIvtJaSkqJRaG3SpElcxxyrFBQUYNeuXfD19cXcuXMxdepUbrHkmltTypMPAGBoaKj1CibvHhdA6ZXpDRs2CN1PVVVSpZ5aisiDEkZSpVhZWSEqKqpcGei0tDQ4OztzH/MGlFbWDA4ORmhoKIyNjTF48GBMnz6dSyy5x+v8/fff2Lt3L4KDg3Hjxg18/vnn+PHHH7nHZYzhxIkTCA4OxqFDh2BjY4MDBw6IHmfEiBEICgoCAMycORMBAQHCut69e+PYsWOix3R0dBSuWHt4eGD79u3COqnPPr9+/Rr79+8XDpZ4FNPo3r07tm3bhlatWuH69euws7PDqFGjkJKSgvbt23Ob+HzlypXC9zI0NBSurq7CutmzZ3M5yaMeQ0WhUCA+Ph6PHj1CcXGx6DErwrNoSNlpEBQKBerVqwdHR0fMmTMHtWvXFjWeOqm7OMtV4MfS0hIXLlyQvNAaUDq2e8+ePQgLC0NOTg5cXFzg7u4uTHfEm1Rza8px8kGu+gfq+zpSNVHCSKqUNm3aVDjXoq51/1ZycjJCQkIQEhKCunXrwtXVFStXruTWLUgbqbrMZGdnC0liSkoKBg4ciJCQEDx69IhLPHWqM72HDh1Chw4dcObMGdy+fRs1a9bkEk995y1VYSM5YqrLzc3FwYMHsXv3bvz555/IyspCeHg4lEoll6ub7dq1E+bQ9PPzw8uXL7Fu3ToUFhaiQ4cO5aY2EYv6Z1v2c5YqMT98+DD8/f1haGgIPz8/rWMpxSR10ZCqQK4CP3IVWhswYAAeP34MFxcXuLq6onHjxhrrpRoPC/CfW1OO8dVyJYxydYUllQdNq0GqlHr16uHMmTPo2rWrxvLTp09rLVoiFisrK3Tr1g0REREwMzMDAAQGBnKLp07qKQk++ugjdO7cGQEBAcLnHBERwS2eiomJCZo3bw4vLy8sW7ZMmI+RV7II6J7PjNdcZ3LEVPniiy8QHx+PPn36YOLEiQgODoaZmRnXREa9TVFRUcK4UENDQ+jr63OLq34utex5Vd7nWUNDQ7F06VI0bdoUy5cvx6effso1XtmiIX5+fujcuTPGjRvHLebVq1dRq1YtNG3aFGfOnMGZM2fQokUL7t2Mpe7iXNGJuUmTJsHOzo5bwnj37l24ubkBKP17vXfvnvAY4FdoLTExEQqFAr/++is2b94sLFclUTzGFc6YMUPnbx+vhLGkpITL++ry9ddfa13Oe+hFVlaWzgJOchRvItKihJFUKevXr8eQIUPg5OSkMXA7JiYGe/bs4Rb32LFjCA0NhbOzM+zs7ODm5ibJzka9y8zo0aPx008/cY+5bt06hIaGwtPTE0OHDv1/7d15WFRl+wfw7wAKqZkmbrnghgiyK2rgrrm9amqAFqHilqFIiri1qGWK5IYalbvk0gybCWkmLuiLmigpKCoqIiIuKMqOMMPz+4PfnJeBAcvmmYMz9+e6vJpzTs59M4wz5z7nee4Hbm5u3AsZoLyde0REBLZs2YLc3FyMGzeOe9ycnBwcPnwYZWVlKl+ojDHk5uZyiZmVlYXg4GAwxoTHyphPnjzhElMpIyMD9erVwzvvvIPWrVvDyMiI+2vcp08feHl5wdTUFI8ePRK6Cz969IjrfE3GGIqKilBWViY8VhaKvArGHTt2YN26dXB0dMTPP//MpROrOvv370fLli3x0UcfCU1DeP5eFy5ciKNHj0Iul6NPnz5ISEhA//79cfjwYRw/fhybN2/mFnvIkCEqQ5zfffddfPzxxzh8+DDOnDnDbYhzZSYmJlxf48oX6WbPns0tVkVpaWlaiVORtbW11mMC4syvrth5u+LUi99++w0ODg7cCsbnz58jLCys2sZYVDDqPhqSSvROYWEh9u3bpzJx28PDQytDZRQKBY4dOwapVIqoqCj06dMHY8aMgaenJ5d4Yi1JAPyvyY1UKsWZM2fg7e2NMWPGoE+fPtxiAsClS5cgk8kQHh6O9PR0BAYG4v3330fbtm01HkuMdT1r6gYL8OnMWlFmZqYwvLqgoAD37t3DqVOnuJ20lZWVQSqV4uHDh3B1dUWbNm0AlHc7fvz4MYYOHcolrrrOocp5d7zulBgYGKBFixZo1aqVSjGhjHn+/HmNx1TSZtOQrl27IikpCUVFRWjVqhUePnwIExMTKBQK2Nracp1Lru0hzmI2+FGHd6O1iusExsXFqYxm2bx5M5fCVaFQ4MWLF1W+wwsLC2FsbMxtJIJY86u1PfUCoCGphApGoqdu376tsqxGx44dtZ6DXC7HkSNHEBoail27dmk9vjY9fvwYoaGhCAsLw4kTJ7QW9+LFi5BKpQgPD8ft27e1Fldf3Lt3D1KpFDKZDNnZ2bh165bWYpeVlSE8PFxto5jX1cuayyjXZuTt9OnTkMlkOHDgAJemIRVPPivPyeJ9Ympra4vExEQAgJOTE5YuXYqRI0cCAOzs7HD58mWNxhOzwY+SNhutiTH3d/bs2XBxcamyoP2OHTtw6dIlbNy4UeMxAXHmV1ecejFq1Chh6gXvNSfFmjtJag8qGIleyc7OhpeXF5KTk2FrawvGGC5fvgxra2vs3LmT2zzGlzW34XH3CxB3SYLExETcuHEDQHlDIZ7rcKlTXFwsDPt94403uAz/CgkJqfE4jzUnxVhb8+9IS0vj0oExNzcX33//PTIyMjBy5EgMHToU33//PdauXQtbW1scPHhQ4zEB8f7NVlZSUoLDhw9DKpVi3759XGJU7AhbEa+mIW3atMHixYvBGENAQAAWL14sxFu9ejXXZmCzZs1CYWEhTE1NIZVKcfPmTRgbG+PRo0cYMWIELl68yC22NonVaK1iYVG5yOBVdHTt2lXtXWnGGGxsbLg1xtL2xQcA+OKLLxAREYHmzZtjwoQJGDduHHr27Ml9zcmMjIwqDYxKSkpw6NAhyGQybp9NpPaggpHolQkTJqB79+7w8/NTGZ65Zs0axMfHc2sE4OTkpHKVGSi/0nz//n08fPiQW7t8MYbMZGdnY/To0Xj48KFQlCclJaFVq1b49ddfua2LFRQUhKKiIixatAhA+UlpnTp1IJfLMXfuXC7NJQwMDGBra4uBAwfCyMioyu83MDBQ4zHFXFvzjz/+wL179zB16lQA5Y0OlPMmly9fjuHDh2s85vvvv4+33noLLi4uOHr0KJ4+fQq5XI4NGzagW7duGo+nZGBggM6dO6Nhw4YAUOV3y3N4qHLxc+Wws+HDh2PMmDFwdXXlEk/bw83EHFYtxhBnMRr8GBgYwMXFBTt27BAarWljMXsx7jBaWFgIFycr49n9XMyLD9qcelGRtj+bSC3CCNEj5ubmr3RM065evco8PT2ZpaUl27p1K7c41tbWwuNFixaxWbNmMcYYKykpYV27duUSc+bMmWzRokVMoVAI+8rKytjixYvZp59+yiUmY4w5OjqyvLw8Ydve3p4xxlhpaSnr3bs3l5jnzp1jfn5+rFu3buyTTz5hx44dY2VlZVxiKT148IBt2rSJDRw4kA0bNoxt376dZWdnc42p1Lt3b5aamips29jYsLS0NJacnMwGDRrEJWbF96lcLmempqasqKiIS6yKAgICmIuLCxs7dizbv38/y8/P5x7z8OHDbPLkyaxNmzbMw8ODHThwgLVt25Z7XDs7O1ZYWMgKCgrU/hHL6tWrtRZLoVAwmUym8eddsGABc3BwYDY2Nszb25v16tWLLVq0iA0cOFD4PObh6NGjbMaMGczMzIyNHz+ehYeHMzMzM27xlAwNDVnTpk2ZqakpMzIyYk2bNhW269SpwyXmoEGD2LFjx6rsP378OBswYACXmIyVv2f27dvH1q1bx9LT04X9CQkJ7Pfff+cWt7ILFy4wf39/1qFDB24xxPpsIrUHFYxEr3Tq1InJ5fIq+0tKSljHjh25xz9//jwbM2YMc3R0ZFKpVKWo4sHGxkZ43L17dxYVFSVs29raconZpUsXtT+XXC5nFhYWXGIy9r8CUSkkJER47ODgwC2u0tmzZ9m8efNYly5dWGRkJPd4jDGWmZnJ1q1bx5o3b8527drFPV737t1VtufMmSM87tmzJ5eYlX932vhdVnT79m22atUq5uLiwlxdXdnFixe5xZJIJKx3797s2rVrwr727dtzi6dUt25d1r59e9auXTvhj3JbG/Grw+N3nZOTw1auXMm8vb3ZoUOHmEKhYBs3bmRmZmZs1KhRGo9nZWXFFAoFy8/PZ2+99ZZwsUMulzMrKyuNx6tMLpezI0eOsClTprCmTZuycePGqXw2iiUnJ0djz5WUlMQ6duzIpkyZwjZu3Mg2bdrEvLy8WMeOHVlSUpLG4vxdvC4+MMZY3759uTzvy4j12URqD1pWg+gVd3d3uLu7IygoSBiPn56ejrlz53JtnnH8+HGsWrUKL168wOLFi7kM3VNHjCUJTExMYGBgUGW/oaEhTExMuMQEgIKCAhQWFgqd8pSdZwsKCpCfn88tLlA+fy82NhZ//vknLC0tuQ8LYlpeW1MpOztbZbviWqJZWVlcYiYmJqJZs2bCkNDnz58L27w7/QLlw/gGDBiArKwsREdHIz09HY6Ojlxi3bp1C1KpFB9++CGMjY3h7u4OuVzOJVZFVlZWtbKhBeMwY8bT01MY4rx9+3YEBgZCLpcjPDycyxBnY2NjGBgYoH79+mjfvr3wGWhoaAhjY2ONx6vM0NAQQ4YMwZAhQ1QarfHqzP139e/fX2PDU62trZGUlCR0P2eMwdnZGd9//z3eeOMNjcRQ52Xzq3mcU/BarullxPpsIrUHzWEkeoUxhvXr12PdunUoLi4GUN4Q5bPPPsPcuXPVFjqaYGBggNatW6Nnz55qm6/wmjspxnydjh07YtOmTVX2M8bg6+vLrZPmypUrcfbsWfzwww/CxYCMjAzMnj0b3bt3xxdffKHxmGvWrMHBgwdhamoKNzc3jB49GvXr19d4nIoqrq3p7u6Od999l2u8iqZOnQozM7MqjXW+/fZb3Lp1i8syImK5cOECZDIZTpw4AUdHR7i7u6N///7cWvRXlpKSInSgrVu3LsaMGYMvv/ySS6za2gGRx5w3a2troQmKQqFAixYtcO/ePW4Xs8Rq8FNbmjZVh8d7Ttvdz8WYX618P1Wn4jqNvGjzs4nUHlQwEr2Vl5cHxpjQ1IKn2NjYGo/369ePew4V8VySQIy1CZU2btyIwMBAFBcXgzGGevXqYf78+ZgzZw6XLqkGBgYwNzcX3kOVY/BojCLm2pq5ubnw8vJCYmIibG1tIZFIkJiYKHQZ5rlEwLFjx4STQSsrKwwcOJBbLKD8dbaxsREaGlX+3fJoaFQdZcdLXs1gDhw4gDFjxnB57n+DR1GhrUYsSmI1+BGr0drfpcnXXazu59q++AAALVu2xKefflrt3Xde76e8vDy8+eabVfYnJydDJpNh2bJlXOKS2oMKRqJXbt++jdTUVGFo5ooVK4QhHl5eXrC0tOQav6ioSFgPsGPHjlyHywDiLUkgtry8PABQ+wWnSWKsm5efn891gea/49atW7h27RoYY7CyskKnTp24xbp79y7Gjh2LRo0awd7eHkD5HfLnz58jMjKSy1IeALB79+4aj0+aNIlLXHWOHTuGdevW4bfffuPy/F5eXtVeUJFIJNi+fTuXuBVlZ2ejcePGKnns2rULkydP1mgcIyMjvP322ypDnBs3bqy1Ic7VCQwMxIIFC7g9f3JyMgICAnDhwgXMmzcP06ZN4xbr79BkwShW93NtX3zQVgx1WrRogXfffRfu7u5aGUlDah8qGIleGTt2LD777DPhjl6XLl2wePFiFBUVISYmBmFhYVzilpSUwN/fH3v37oWZmRkYY7h37x48PDywevVqbnNZxBgyU9PahBKJhNvcGTHWROzXr99L7x5rmlgnDIA4w9yGDx+OqVOnVmnbHhoaim3btuHIkSMajwkAfn5+WLt2LZfnrs7x48cxc+ZM3L9/H6NHj8aSJUswefJkyOVyLF26lNsyDOoK0ZSUFKxfvx5GRkYaX4ph+fLlGD9+PLp06YKioiIMGzYMSUlJMDIyws8//8xlqHxtx+vfdXx8PFauXIn09HQsXLgQrq6u3KZe/BO9evXCuXPnNPJcnTt3RkpKyj8+9m+JcfFBrOHjyuU0QkND8ccff8DFxQXjx4/HyJEjuV/4JrUDFYxEr3Tv3h0XLlwQtl1cXBAXFwegfM3C//73v1zizpw5E3Xq1EFAQIBwZS4/Px+LFy/GixcvsGXLFi5xxRgy4+/vX2WfQqFAWFgYHjx4gNLSUp2JK8aXt5jzzSoPc1Nezec5zK2mddR4rrEmRmFub2+PtWvXonfv3oiOjsbEiRMREBAAHx8freVw+fJlrFy5EteuXYO/vz8++ugjjc/btLKywtWrVyGRSBAcHAyZTIaYmBjcvn0bHh4eKp/RvGh7iPPLaPrftViN1oqLi/Hs2TO0bNlSyEPZL8DZ2ZnLOrzm5ua4fv16lfdpaWkpLC0tuc2bF0NZWVmVgj8zMxMymQwymQxnzpzhnoOyeJTJZDh27Bj69OmDX375hXtcIi7qkkr0Sk5Ojsq2slgEwHUo0rFjx3Dz5k2VfQ0aNMDGjRvRuXNnbnHr1q0rPDY0NESbNm24FosA8N133wmPS0pKsH37dgQFBeG9997DwoULdSrukydPEBwcXO1xHg0IMjMzaxy6xnNuXXx8vMq2cphbfn7+S+dqvSrGGAoKCqoMgcrLy0NZWRmXmED58HHlsFt1rKysuMQdNGgQAOCDDz7AkiVLtFYsxsXF4dtvv8WzZ8+waNEivP/++9xiGRsbCxcbjhw5Ag8PDxgZGcHCwoL73Dp1Q5zDw8Ph5+fHdYjzy2h6jvXgwYOFRms7d+6sMnec1zBNPz8/2NjYYObMmQCA6dOno1+/figsLMTvv/+OjRs3ajymWN3PlbR58UFZLD58+BBhYWGQSqW4cOEC/Pz8uLy26tSpUwfvvPMOWrZsCWNjYzx48EArcYm4qGAkeqVDhw44evSoMIdRKSYmhst8M6XqTjorNyXQNLGWJMjPz0dwcDC2bNmCUaNGISYmRvgi50nbceVyOZ48ecL1d1iZiYkJunbtqrV46lQe5rZr1y5uw9y8vb0xZMgQrF69WjjBT0hIwOLFi7l2BMzIyMCsWbPU/m4lEgmOHz+u8Zh5eXk4dOiQsK1QKFS2R4wYofGYANC3b1/cvXsXfn5+GDx4MIDyiwFKmi6OGzZsiNjYWDRp0gSxsbHCRReFQoGioiKNxqps5syZWLJkidohzp988gm3Ic4vo+nPkBMnTmj0+f6uM2fOYPPmzcJ2w4YNsWPHDgDlo3h4WLFiBdavX49evXqp7X7OixgXH3788UdIpVI8evQI48aNw6ZNmzB27FisWLFC47EqS0hIgEwmQ1hYGJo3bw53d3ecPn0arVq14h6biI+GpBK9cvXqVbz//vvo168f7O3tIZFI8NdffyE2Nha//vortxPxmTNnom7duli9erUw3r+wsBALFy7kOiRVDF999RX27t0LDw8P+Pr6okmTJjobV4xhi2LOYRRrmFtERATWrVsnFDFWVlaYO3cuPvjgA24xxRj6W1OHYYlEIpx4a1r//v1rbHqj6eL4+vXr8PX1xaNHj+Dj44OpU6cCAI4ePYrDhw9j3bp1Go1XkVhDnCvSVoMfQPuN1ipOgwDKO/wqm8lVPsaDNrufizG/un79+nB0dMSKFSuEXgwdOnTQ+Dzjyjp16oQmTZoId3OVS3QR/UEFI9E7RUVF2Lt3r0qXRw8PD65fpC9evIC/vz/27duHdu3agTGG9PR0fPjhhwgMDOQ+TFTbQ2YaNGigMuwM4L/sgxhxxSgqhg0bht9//12rMZXEWk9UDGL8bvfv348PP/xQqzFrm+LiYq6fhxYWFkhISFA7xLlbt24ab5AiVoMfsRqtOTo6Ijw8HO3bt1fZn5qaig8++IDLv6moqCjY2toKo4SWLl2KyMhImJmZYcOGDdzWYxTj4kNBQQEOHjwIqVSKxMREjBw5EmFhYcjMzNR4rIrS0tKqvWMql8thZEQDFnUeI4RU4eLiwuV58/Pz2eXLl9nly5dZfn4+lxgVpaWlMQcHBzZgwAA2d+5cNnfuXNa/f39mb2/P7ty5wz2+rlMoFGr3P3/+nAUGBmo1l5SUFObt7c01xsmTJ2v8w0tkZCTr3bs3a9KkCWvSpAnr3bs3i4yM5BaPMcb+/PPPao+dP3+eS0wHBwcuz/sya9asER7LZDKVY4sXL+YSMyMjg50/f569ePGCMcbYo0eP2Oeff85at27NJZ7Shg0bmLOzMzt9+jTLy8tjeXl5LDY2ljk7O7P169drPJ6lpSUrKytjjDH2/fffs379+rHS0lJ2/fp11q1bN43HU/rkk0/Y7NmzVb5n8vLy2OzZs9n06dO5xT1w4ADr3LkzCwkJYYmJiSwpKYmFhISwLl26cPs3a2NjwwoKChhjjEVERLBOnTqx+Ph4tn37djZo0CAuMRljrHPnzmq/x3Nzc5m5uTm3uBXjhISEsJEjR7LWrVuzKVOmcIvVt29f4fHHH3+sckyszy2iXVQwEqKGvb29Rp8vNja2xj+8DBs2jIWGhlbZL5PJ2JAhQ7jFrez+/ftsw4YN7N133+UWo6CgoMY/PKSnp7MZM2awESNGsB9//JHl5+ezefPmMVNTU+bj48Ml5uXLl9l7773HrKys2KJFi1hmZiYbN24ca9myJVu7di2XmGJSvm9OnjzJcnJyWG5uLjtx4gR799132YYNG7jFlcvlbN++fSwwMJBdvnyZMcZYVFQUc3FxYXZ2dlxiinXiVTFu5Rx45LR+/XrWpEkT1qtXL2Zra8u2bt3KGjVqxHx8fFhGRobG41UWHh7OXFxcWOPGjVnjxo2Zi4sLCwsL4xKr4nfJ6NGj2ZYtW9Qe07ROnTqp3V9WVlbtMU1JSEhgHh4ezNHRkTk4ODAPDw+WkJDALV7Ff4+enp4qF+t4/pvS9sWHmjx//pzt3LmT2/NXfK9Wfk15vo9J7UH3kAlRQ9Md6/r37w9bW1uhIyqrMBJcIpGgb9++Go2ndOfOnSrzKwDAzc0NX375JZeYStru4lbTNdbYaAAAIABJREFU/FOJRMJljsfEiRPRu3dvjBs3DocOHYKTkxNsbW1x+fJlvPPOOxqPB5R3HZwxYwZ69+6N3377Dfb29vD09MSePXu4z09SLqtRnfPnz2s85g8//IALFy6gQYMGwr7+/fvj999/h5OTE3x9fTUeEwCmTp2Ku3fvolevXvDx8UGbNm1w/vx5rFq1itvcyaSkJDRr1qzKfsZ5OHfFzyNWaZZK5W1N+Omnn3D9+nWYmpoiLS0NFhYWiIuLQ/fu3TUeS51x48ZxW9OyMrEa/FT3e+PdaA0oH869Z88elX1Pnz7Fli1bMGPGDI3HMzAwQHZ2NurXr4+jR4/Cz89POKZsgsODr68v2rRpg0WLFqnMr543bx63zwh/f3+Vz2CJRAJTU1P06dOHyxzYinFe5RjRHVQwEqIF+/fvR1hYGO7fv49Ro0bB3d0dHTp04B6XibAkgVhd3FJTU7X+xZWdnY1vvvkGADB06FC0atUK+/bt47ow9osXL4QmIRYWFti8eTPWrFnDLV5FYWFhWolTEWNMpVhU4t3U4vz580hKSoKhoSGKiorQokULpKamcm2mZG1tLcoam4wxFBUVoaysTHisLCp4FBdvvPEGTE1NAQDt2rWDhYWF1opFADhw4ADWrl0rzOu2tLSEn58fxowZo/FYP/30k9DgZ+3atUJHyePHj3PreguUL6sxZ84ctY3WtLXm5LNnzxAZGQmpVIqUlBSMHDmSS5ylS5fCwcEBZWVlGDp0KOzs7AAAp0+f5r5MijYvPgDlnxGVZWdnw8/PD66urty6wmZlZSE4OBiMMeExUP758OTJEy4xSe1CTW+IXlFXPKnDq+FFQUEBoqKiEBYWhocPH+Kbb77BgAEDNB5HKSgoCDKZTO2SBG5ubvjss880HlOsLm5t2rSBq6srxo8fj169enGNpWRvb4+zZ88KJ9XOzs4q2/Xq1dN4TEtLS0RERAgxXF1dER4eLmzzWh8QAD766CNMmDABw4YNU1njk6fhw4dj2rRpVa7Yh4WFYevWrdyWQajcjVYb3WnFaLQDlBdtBgYGVUY+KO9savrfbvPmzTFp0iQA5SecISEhwjbAdy3RoKAgSKVSrFq1Cg4ODpBIJLh48SKWLFmC8ePHc7tjrQ7PBj9iNVrLzc0VisQbN25g9OjRkMlkuH//Ppd4SnK5HHl5eWjcuLGwr6CgQOWC0/HjxzVeLGvz4kNNiouL0atXL1y6dInL879snd2lS5dyiUtqDyoYiV75uyd92dnZePvttzUev6ioCAcPHkRoaCgyMzPx7bffci0YAe0vSSBWFzflENjQ0FCkp6fjgw8+wPjx4+Hk5MQtproTbSVew2Brer/wWh9QSSaTQSaTIS4uDu+99x7c3d0xdOhQ1KlTh1tM5VpnjRs3hoODAwDg4sWLeP78OSIiIqp0Y9SU+vXrC8OcGWNITk5G165dhSKKx/Db4OBgrmtL1ha7d++u9phEIsHEiRO5xe7SpUuVIc5AeaHj5OSEGzduaDzm/fv3kZmZCTs7O9StWxePHz/Gxo0bsXv3bty7d0/j8SoqKChQWVbj71ww/TdMTEzg5OSE1atXw9nZGQDQvn173Llzh2vcv0PTF31q08UHQLwLThs2bOBy8ZnULlQwEr0i1geqspDJyMjAyJEj4e7uzq3Vd22Sl5eHAwcOQCaT4dKlSxgyZAi2b9/OPe6DBw8QHh4OmUyGzMxMuLq6IiAggHvcipSFhTbl5eXhzTff5B6nsLAQUVFRCA0NxdmzZzFkyBCMHz8ew4YN4xYzJiZGuOhhaWmJwYMHc3197969W+NxZQt/TVq+fHmN6yHymnecnp5e4/G2bdtyiVvZ06dPER4ezmWem5KFhUW1RWFNx17Vhg0bsGLFCpibm6OwsBA+Pj7w9/eHp6cnFi5cyG3R81OnTtV4nNe8+W3btiE0NBR37tzBBx98ADc3N7i6unIfYfJ3aPr7X4yLD4WFhVX2PXv2DCEhIbh8+TJ++eUXjcd8mbZt2770M4S8/qhgJHql4lAodXgNhTIwMICNjY3Q9KbySSHP9etqy5CZ58+fY/v27SoNCXjKyspCWFgYfvjhB+Tm5iItLU0rcc+ePQupVIrIyMiXFh2aUPGO7vnz57nfya0sPj4e06ZNw5UrV6BQKLQaW9snKiUlJTh8+DCkUin27dun8ef//vvvq+x79uwZtmzZgpycHOTk5Gg8JlD++dS5c2dhXmjloak87qYqqZvntmnTJm7xtD3E2dLSEqdPn9Z6gx8DA4MaG63xXjNVWfxLpVKcOXMG3t7eGDNmDPr06cM1bk00fYdR2xcfgPK7tRUbFxkYGKBJkybo27cvvvzyS7z11lsaj/kybdq04X6nnIiPmt4QvWJiYlJjN01eTpw4ofWYQM1DZu7evavVITONGjVCUFAQ14IxKysLERERkEqlSE1NxdixY/HTTz/h3Xff5RYTKC+afvnlF0RGRuLRo0cICgrCkiVLuMUrKipCdHQ0fvnlF8THxyMnJwfh4eHo378/t5gV3bhxAzKZDKGhoTAyMsKECRMwfvx4rcSuSBvXO0tLS3HkyBFIpVIcOnQIw4cP59bkYtasWcLjx48fY+3atQgNDcX06dPh4+PDJSYArFq1ClFRUWjWrBnc3d0xatQorkMX1c1zu3LlCvd5bkB5U66xY8ciODhY7RBnTROrwY9YjdaUmjRpghkzZmDGjBl4/PgxwsLC8NVXX4n2XQho/vOiQ4cOCA8PV3vxgVeznZqG9v7555/o2bMnl7jq7mwC2vkMJrUD3WEkekUbjStqEzGGzNSE55XIQYMG4ebNmxgzZgzc3NzQu3dv7kNCFy1aJMyjc3Nzw5gxY+Dk5MR1vs6ECRNw7tw5DB06FK6urhgwYADMzc21Mkfom2++gUwmg0QigZubG8aPHy/cwRADzzuMv//+O6RSKY4dO4a+ffvCzc0Nc+bM4X7X+O7du1i9ejViYmIwa9YsTJ8+nUvzJHVSU1Mhk8kQHR2Nli1bYvHixXB0dNR4nNowz01bQ5zFbPADaL/RWk14/Xvdv38/Pvzww5f+f7Nnz8bmzZs1Fles+dXV4fl5WPnOJqC6REttmKNK+KI7jESvqFvjTBte1pmNV6MSsZYkqA7PAu7LL79Ev379tDpvcP/+/WjZsiU++ugjjB07Fg0bNuQePyMjA/Xq1cM777yD1q1bw8jISGs/s0KhgEwmg6WlpVbiAVXXHVNijHEbogkAI0aMgIuLC/744w906dIFALi1rFeaOHEi4uLi4Ovri5UrVwqdaJVX93kXjh06dMCAAQOQlZWF6OhopKencykYN2/ejNDQUEyePFmY56bt+b6DBw/G4MGDVfbxOOGuXBBW3NbGz2xgYCDE4bWUkti+++67v1UwarJYBMrnMSckJKhcfBg6dCj3+dXV4Xn/hwpCQncYiV4Rq7mDpaUl6tatKzQGMTY2VjnOa5isGEsSNG3atNoT/OfPn6O0tFTjMYHqCwslXlfylXMWo6OjYWVlhXPnziElJQWNGjXiEg8AMjMzhY6lBQUFuHfvHk6dOqV2jS5N+u2332p8jXmsK1dTR00ANc5J/jdSU1MhlUohk8lgbGwMd3d3bNiwgeucyXbt2gmvr/K/yq9oXl13AeDChQuQyWQ4ceIEHB0d4e7ujv79+8PQ0JBLPKXaNs9Nm3OxeDf4qY2N1njdAauNI4fEaATDM2bFu7hxcXFwcXERjm3evBmzZ8/mEpfUHlQwEr0iZnOHK1euQCaT4ciRI7CwsNDpJQnEIGarfqXTp09DJpPhwIED6Ny5M44dO8Y95r179yCTySCVSpGdnY1bt25xi+Xl5VXtMYlEgh07dmg8plwuh5GRuINhUlJShOKxbt26GDNmDLeOpWJQNuUaOHCg2jvWvIdNAhDmuYWGhoo2z433Sb42G/yI1WitphEByuZNmlanTh2V9RcrxpRIJHj8+LHGY74Mr4sPTk5O1b6+SUlJKC4u1nhMQLUoF2ONWiI+KhiJXlm9erVWmztUZ8+ePfD19cWiRYvg7+/PPZ62lySobebPn481a9Zwe/4nT54IjS2A8i/v2NhYbk1osrKycPfuXXTq1EnlTmZaWhq3Zgsvo675gyZUPBnx9vZGcHCwxmP8E8nJyZDJZFi2bJnGn1usq/i7du2q8fNA03dxY2Nj0a9fPwDlQ90qXrji9T5S0nZBI9ZC9rGxsTUeV77+mibGiACxlsuqCa+LD2Is9wOovsaVX+/a+PoTzaOCkeglbTV3qCg5ORlSqRRHjhyBubk5XF1dMXz4cGGekrbp09pJvH7WqKgoTJkyBUZGRjA0NIRMJhOaePCybds2LFmyBB07dsSdO3ewZcsWjB49mmvMv4PXa1zxZESbV7Jv3ryJ+fPn4/bt27CxscHatWvxzjvvcI1ZG6/i87jDK+bPqe2CpjY0+NEmMUYEiFWwiHE3FShfKuvWrVuwsbHB0KFDucSorDZ+NhHtoqY3RC9pq7mDkrW1NQwNDeHu7o7g4GCYmJgAgDB80MrKilvs6tC1on/v888/x+nTp9GlSxf8+eefWLBgwUuv7P9b69evx9WrV9G0aVOkpqbCw8OjVhSMvN5PYt0JnzJlCiZOnIi+ffvi119/hY+PD8LDw7nGrPgaVn49ef577devn/C+9fT0xM8//ywc69Gjh8ZPBsX6OQHAw8NDqwWNWA1+xGq0VvH9oq0RAdOnT+ceQ52a5oxv3LiRS0xvb29cvXoVzs7O+PLLLxEfH48vvviCS6yKEhMT0axZM6EXgbKBIO/mY6T2oIKR6BV1zR0CAwO5N3cwNTWFRCJBTEwMYmJiVI5JJBJuX9410bUhqcoht5UxxiCXy7nENDIyEjpo9uzZE3l5eVziVGRsbIymTZsCKL/wUVJSwj3m38Hr/VTTiQrP+Ul5eXnCieiCBQu4j0AAVF/Dyq8nz3+vubm5wuOrV6+qHONRwIn1cwLaL2imTZuGadOmCU1u/P398eDBA/j5+XFt8PPgwYMaG63xUvH9cu7cOa3EzMrKwtdff632mEQi4TbfWNsXHwDg1KlTuHz5MgwNDVFYWIi+fftqpWDk9R1KXh9UMBK90qNHD5XmDkeOHFHpFMqrucPJkye5PO/LiLUkgRgqLnpemYWFBZeYWVlZKieclbe9vb01HvP+/ftYsGABgPLfY0ZGhrAN8G1Q8rIOuDyIdaLy4sULXLt2TTgBLi4uVtnmMSpArKv4NRVpPAq4GzduoEePHmCMCY+B8p8zJSVF4/EqEqOgAbS/kP21a9eERmuffvqp1hqtiXEhsuL8caVnz54Jw0J5FYxi3E2tW7eucIG7Xr16OrtUCql9qGAkemXnzp1q9z958gRvvvkm19gPHjxAcHAwrl27BqC8+Yy3tzdatmzJLaYYQ2bEIkZnxWnTpiErK0vtNq8Tp+rWddPGe7jiz6rrWrRooVLwN2/eXNjmNSpArOJYeaGDMaZy0YMxhidPnmg8Xs+ePREYGCjcKdcmbRc06hr8NGvWDN7e3mjevDnX2NbW1rC2tsbXX3+NPXv2YNKkSdwbrYkxIqDixcLHjx9j7dq1CA0NxfTp0+Hj46PxeEpiXHyofIGl4sUX3p3eiX6jpjdEbz179gwRERGQyWS4ceMGRo0axa3F+fHjx+Hl5YUpU6YIy1v89ddf2LFjB3bs2IFBgwZxiVsbliTQllOnTtV4vG/fvlrKpHyB7JiYGAwZMoRrHG2+h8n/5OXlcS/OtWn58uU1Hl+6dKlG423btg1r1qzBnDlz8Omnn2q1iDMyMsLbb78tFDTK5Rh4FTRiNgupbY3WeLp79y5Wr16NmJgYzJo1C9OnT0e9evW4xqzpd8uLWF1SCaGCkegVsVqcOzo6Yu/evbC0tFTZf+3aNXz00UfcOrzVtiUJeHJzc6uyTyKR4Ny5c7h//z4UCgXX+IwxnDhxAlKpFIcOHYK9vT2ioqI0Hkes97C+KygowMGDByGVSnH+/HlkZmaKndJrLScnB8uWLUNcXBwmTpwIAwMD4RiPodxiEWs5goqN1oYPHy40WlMSo9EaLxMnTkRcXBx8fX0xceLEKgUxr8JR2xcfgPKLANX97g4ePFgrGqAR3aQftx4I+X/NmjWr0uL8wIED3OMWFxdXKRaB8mGpL1684BZXrPk6YggNDVXZPnz4MFatWgVzc3MuC8orxcbGCkWig4MDzpw5g9u3b6NBgwZc4on1HtZHRUVFiI6Oxi+//IL4+Hjk5OQgPDyc2/qaYtm6dSsGDBiATp06oaysDF5eXoiMjISZmRl27dqFbt26aTymiYkJGjdujPz8fGRlZakUjLpErAY/tbHRGi+nTp2CRCLB+vXrsWHDBgD/++6TSCRITU3lEleMIeTu7u4YMmQIli9fLoxyuHHjBubMmQMjIyMqGAk3VDASvSJWi3MTExPcuHGjSvOVa9euce1ep2udUP+O0NBQBAQEoG3btvjuu+/Qs2dPbrHeeecdtG/fHrNnz0ZgYCAaNGiA9u3bcysWAfHew/pmwoQJOHfuHIYOHYqZM2dCKpXC3NwcgwcPFjs1jduwYQO8vLwAAHv27EFSUhJSU1ORmJiIOXPmIC4uTqPxIiMjsWTJEri5uSEhIaHK3S9dIlaDH7EarYkhLS1N7BS05q+//sKaNWvg6OiIzz//HFeuXMHhw4cREBCAUaNGiZ0e0WFUMBK9IlaL8++++w5DhgzBtGnThDmMFy9eFOYw8iLWkgRi2LFjB9atWwdHR0f8/PPPWhlyNWXKFERERGDLli3Izc3FuHHjuBdvYr2H9U1GRgbq1auHd955B61bt4aRkZHOFuZ16tQR5jr/9ttv8PLygqmpKQYOHIj58+drPN7WrVsRHR2Njh07avy5axsxG/yI0WhNDKdOnUJOTk6Vgik8PBzNmjXTqc/EOnXqYPHixSgrK8O0adPQvHlznD17Fm3bthU7NaLjaA4j0XvKFuehoaHcOm3m5OTg6tWrOHTokLBeoKWlJYYNGwY7Ozs0bNiQS1x9YmBggBYtWqBVq1YqJ/ba6B536dIlyGQyhIeHIz09HYGBgXj//fe19iWujfewPsrMzIRMJoNMJkNBQQHu3buHU6dO1dh9+HXUq1cv7Nu3D40aNYK5uTnOnTsHc3NzAOWfU8qCg/xzYjX4EavRmhicnZ0RGRlZpetsZmYm3N3d8d///lekzDTv0qVLmDVrFszMzBAYGIijR48iICAAn3zyCXx9fbmvKU30FxWMhGiBp6cnJk2aVGU4W3R0NEJDQ7F7926RMtMdtaV73MWLFyGVShEeHo7bt29rJSbh7969e5BKpZDJZMjOzsatW7fETkljzpw5g2nTpuHJkyfw9vbGsmXLAJTPA969ezd++eUXcRN8zYnR4EesRmtisLW1RWJiotpjdnZ2uHz5spYz4sfe3h5BQUHCUi1AeSO0r776CseOHUNSUpKI2RFdRgUjIVpgbW2NK1euqD1mY2NDH/IadPv2bZUhWNoc9lZcXCwspPzGG2/o7BBGfZeWloZ27dqJnYZWpKSkoHPnzmKn8Vp78eIFVq9ejV9++QVubm4qBaOmlyxRsrKyEkaz/JNjr6POnTsjKSmpSj+AoqIi2Nra4ubNmyJlpnkKhaLau4hXr15F165dtZwR0Re62ZaMkFqmuLi42mM8u6Tqk+zsbLz//vsYNmyYMOxqyJAheP/995Gdnc0lZlBQEAICAoRtc3NzWFtbo0uXLkK3PvJ6+uOPP7B9+3Zhe8SIEejRowd69Oih80M0b9++jVWrVsHe3h6urq5ip/Nai4yMhL29PeRyORISErB8+XIsXbpU+MOLstFaZbwbrYnB09MT7u7uyMjIEPbdu3cPH330ETw9PUXMTPMqfq9U7gy+d+9ebadD9AjdYSREC8aOHQtXV1d4eHio7N+3bx+kUil+/fVXkTLTHRMmTED37t3h5+cn3NljjGHNmjWIj4+HTCbTeMxu3bohNjZW6IqqXFdNLpdjwIABOH36tMZjEu3o06cPQkJC0L59ewDlw96ioqJQWFgIHx+fKksVvO7u3r0rDLlVKBRIT09XmctIXs2IESOwadMmrTf4OXbsGKZMmVJtozVdmsMIlF+8W7NmDYqKiiCRSGBiYgJ/f3/4+Pjo1EiPimsrV3ysbpsQTaIuqYRowaZNmzB27Fjs2LED9vb2kEgkSEhIQG5uLq2hpyEJCQlV5lpJJBL4+/tzG1JXVlamsoTGvHnzAJQv6FxQUMAlJtGO4uJioVgEgAEDBgjzYPPz88VKi4uePXuiqKgIH374Ifbv3w9zc3O0b9+eikUNOHTokChxu3fvjv379+PQoUNCJ25LS0vs2bMHdnZ2ouTEk6+vL3x9fZGXlwcAwhqFFe3Zswcff/yxtlPTqIr3eCrf76H7P4QnKhgJ0YLWrVsjPj4ex44dQ3JyMhhjGDZsmE6u6SYWxpja+R2lpaXCvEJNKygoQGFhIerVqwcAwvCngoICnSsq9E3lYcxBQUHC46ysLG2nw5W5uTlOnz6NlJQUpKamokOHDjp1V0YfzZ49G5MmTcKKFStU9kdHR8PHx0dnG62pKxSV1q1bpxMFY1FREcrKyoTHykKRCkbCEw1JJYTohM8//xzXr19HUFAQWrduDQBIT0/H3Llz0blzZ6xatUrjMVeuXImzZ8/ihx9+EGJmZGRg9uzZ6N69O7744guNxyTaMXXqVJiZmeGrr75S2f/tt9/i1q1b2Llzp0iZ8fHixQscPnwYMpkMcXFxePbsGfbt24f33ntP5+a86QNqtFaVcsrA66xdu3YwMDBQKQ4lEomwfFRqaqqI2RFdRgUjIUQnMMawfv16rFu3Tmgy9MYbb+Czzz7D3LlzVToTatLGjRsRGBiI4uJiMMZQr149zJ8/H3PmzKG7NK+x3NxceHl5ITExEba2tpBIJEhMTIS1tTV27tyJt956S+wUuSkuLkZ0dDRkMhliY2Px6NEjsVMi/1CnTp2qXfqlc+fOSElJ0XJG4qM5foS8OioYCSE6Jy8vD4wxNGzYUKsxgZqHRJHXz61bt3Dt2jUwxmBlZYVOnTqJnZJWVRxyTV4f1GitKl24w5ienl7j8bZt22opE6JvqGAkhOiEkJCQGo9PnDhRJ2IS7dCnE7PExEQsX74cjRo1wsKFCzFr1iycPXsWHTp0wNatW9GzZ0+xUyT/UEZGBsaOHYuGDRuqbbSmHEKvT1auXIklS5aInca/4uTkVGWfRCLB/fv38fDhQygUChGyIvqACkZCiE7w9/evsk+hUCAsLAwPHjxAaWmpTsQk2uHk5CTMDQIgDC/WxROznj17YuHChcjJyYG/vz+2bNmC//znP4iLi8PChQsRHx8vdorkFVVstGZlZaWTjdZKSkqwbds2HDx4EJmZmZBIJGjZsiVGjRqFadOm6fQc3OTkZAQEBODChQuYN28epk2bJnZKREdRwUgI0TklJSXYvn07goKC4OLigoULF3JbWkPMmER7dPnErOJQPQsLC5UF3+3t7XHp0iWxUiPkpVxdXdG2bVtMnDhRpflYSEgI0tLSEBERIXKGmhcfH4+VK1ciPT0dCxcuhKurK7d5+oQAtKwGIUSH5OfnIzg4GFu2bMGoUaMQExPDfeiVGDGJ9lQ+Mdu1a5fOnZhVbM5Uv359lWO69rMS3ZOUlISwsDCVfaamprC3t9e5i3bHjx/HqlWr8OLFCyxevBjDhw8XOyWiJ+gOIyFEJ3z11VfYu3cvPDw84OvriyZNmuhkTKId+nRiZmRkhLfffhuMMTx//hyNGzcGUN55OCcnByUlJSJnSEj1Bg4cCDc3N3z88cdC07G8vDyEhIQgNDQUJ0+eFDdBDTIwMEDr1q3Rs2dPtV24ZTKZCFkRfUAFIyFEJxgYGKBBgwYwNjZW+SJVrk/1+PFjnYhJtINOzAh5PTx9+hSrVq1CVFQUcnNzAQBvvfUWRo4ciUWLFsHU1FTkDDUnNja2xuP9+vXTUiZE31DBSAghhFRCJ2aEEEJIOSoYCSHkFRUWFtZ4nNavI4QQvs6cOQNra2s0bNgQBQUFCAgIwKVLl2BpaYklS5agUaNGYqeoMcruzdU5f/68FrMh+oQKRkIIeUXt27ev9phEIkFqaqoWsyGaRCdmhLweunbtisTERBgaGmLq1Klo0aIFxo4di5MnT+L06dP49ddfxU5RY+7evVvjcTMzMy1lQvQNFYyEEPKKlHMVie6hEzNCXg9dunTB9evXAQDdunXDxYsXhWO6vixMcXExysrKANCIFsIX9csmhJBX1LZtW8ydOxfnzp0TOxWiYYsXL8bly5fRsmVLmJmZVflDCKkd3n33XQQEBKCkpATOzs74/fffAQDnzp1DgwYNRM5Os4KCghAQECBsm5ubw9raGl26dMH69etFzIzoOioYCSHkFcXHx6Njx45YuHAh2rdvj/nz5yM+Pl7stIgGjBkzBiEhITAzM8PEiRMRHR2N0tJSsdMihFTy448/oqCgABYWFvj9998xYsQING7cGOvXr8fPP/8sdnoaFRISgtmzZwvbpqamSE1NRWpqKiIiIkTMjOg6GpJKCCEa8ODBA4SHh0MmkyEzMxOurq4qV4LJ66mwsBBRUVEIDQ3F2bNnMWTIEIwfPx7Dhg0TOzVCSCU5OTkoLS1Vu5TG8ePHMXDgQBGy0hwHBwf89ddfwvbPP/8MT09PAICjoyMSEhLESo3oOCoYCSFEQ7KyshAWFoYffvgBubm5SEtLEzslokHx8fGYNm0arly5AoVCIXY6hJB/QBcKqs6dO+PSpUtV5isWFBTAwcEBKSkpImVGdB0NSSWEkH8hKysLP/30EwYOHAgnJyekpKTgp59+omJRR9y4cQPffPMNbG1t8cknn2DChAm4efOm2GkRQv4hXbg/MnnyZIwfPx4ZGRnCvoyMDHh4eGDgIQX7AAARmElEQVTixIkiZkZ0nZHYCRBCyOtq0KBBuHnzJsaMGYPly5ejd+/e1DVVR3zzzTeQyWSQSCRwc3NDWFgYOnfuLHZahJBXpAufzUuWLMHGjRvRq1cvFBcXAwDeeOMN+Pv7w8fHR+TsiC6jIamEEPKKTp48iX79+unEiQhRtWzZMowfPx6WlpZip0II0YDK8/9ed3l5eQCAN998U+RMiD6gO4yEEPKKfvvtNxw6dKja44GBgVrMhmiSk5MT7ty5gzt37qg9PmLECC1nRAj5N1xcXMRO4V/z8/PD2rVrAQA7duyAr6+vcGzy5MnYtWuXSJkRXUcFIyGEvCJra+tqj9Fdx9dbWFhYtcckEgkVjITUElFRUbC1tRXWR126dCkiIyNhZmaGDRs2oGPHjgCAzZs3i5mmRpw4cUJ4vHv3bpWCMTExUYyUiJ6ggpEQQl7RpEmTqj02f/58LWZCNG3nzp3VHgsPD9diJoSQmnz++ec4d+4cACAyMhL79u3D/v37kZiYiE8++QQxMTEiZ0jI64+6pBJCCAcymUzsFAgnc+fOFTsFQsj/MzAwEJaZiIyMxIwZM9C9e3dMmTIF2dnZImenWUVFRbh27RquXr2K4uJiXLt2DcnJycI2IbzQHUZCCCHkH6BecYTUHgYGBsjOzkb9+vVx9OhR+Pn5Ccd0rYhq0aIFvL29AQDNmzcXHiu3CeGFCkZCCHlFycnJavczxiCXy7WcDdEWmp9KSO2xdOlSODg4oKysDEOHDoWdnR0A4PTp02jXrp24yWlYxTmMhGgTLatBCCGvaMCAATUepy/311fTpk3VFoaMMTx//hylpaUiZEUIUUculyMvLw+NGzcW9hUUFIAxhgYNGoiYmeZduXIFDRs2RNu2bXHmzBmcOXMGHTt2xNixY8VOjegwKhgJIYQQQshrS10R1aFDB4wbN07s1DRq4cKFOHr0KORyOfr06YOEhAT0798f58+fh6WlpU50giW1ExWMhBDyik6dOlXj8b59+2opE0II0U/6VER17doVSUlJKCoqQqtWrfDw4UOYmJhAoVDA1tYWV69eFTtFoqNoDiMhhLyiTZs2VdknkUhw7tw53L9/HwqFQoSsCCFEf0RHR9dYROkSY2NjGBgYoH79+mjfvj1MTEwAAIaGhjA2NhY5O6LLqGAkhJBXFBoaqrJ9+PBhrFq1Cubm5tixY4dIWRFCiP7QpyIqKysLwcHBYIzhyZMnCA4OBgBhmxBeqGAkhJB/KTQ0FAEBAWjbti2+++479OzZU+yUCCFEL+hTETVt2jRkZWVVeQwAU6dOFSstogdoDiMhhLyiHTt2YN26dXB0dMSiRYtgZWUldkqEEKJXli9fDqDqcjfK09ulS5dqPSexBQYGYsGCBWKnQXQIFYyEEPKKDAwM0KJFC7Rq1UrlZIUxBolEgvPnz4uYHSGE6IfIyEisXbsW165dAwBYWVlh3rx5ervUhKOjIxISEsROg+gQGpJKCCGv6M6dO2KnQAghem3jxo2QSqVYuXIlHBwcIJFIcPHiRSxZsgTp6enw9fUVO0Wto3tBRNPoDiMhhPxLt2/fFq5sW1paomPHjiJnRAgh+qFLly64cOECGjRooLI/NzcXTk5OuHHjhkiZiYfuMBJNozuMhBDyirKzs+Hl5YXk5GTY2NgAAC5fvgxra2vs3LkTb7/9tsgZEkKIbmOMVSkWAaBhw4YiZFM70L0gomkGYidACCGvK29vb/Tp0wcpKSmIiIhAREQEbt26hd69e2PmzJlip0cIITqvQ4cOCA8Pr7I/LCwM7dq1035CWpadnV2lQNTHYbiELxqSSgghr6hz585ISUn5x8cIIYRoxt27dzF27Fg0btwYDg4OAICLFy/i+fPniIyM1Kmicfny5Rg/fjy6dOmCoqIiDBs2DElJSTAyMsLPP/+MoUOHip0i0VE0JJUQQl4RYwwKhQKGhoYq+0tLS1FWViZSVoQQoj/MzMyQkJCAmJgYJCcnAwCGDh2KwYMHV1lq43UnlUrx1VdfAQB27twJiUSCx48f4/bt2/Dw8KCCkXBDBSMhhLwid3d3uLu7IygoCK1btwYApKenY+7cuXBzcxM5O0II0R+DBw/G4MGDxU6DK2NjY6EIPnLkCDw8PGBkZAQLCwsoFAqRsyO6jOYwEkLIK1qxYgVcXFzQq1cvmJqawtTUFC4uLnB2dsa3334rdnqEEEJ0SMOGDREbG4srV64gNjYWI0aMAAAoFAoUFRWJnB3RZTSHkRBCNCAvLw+MMb3uzEcIIYSf69evw9fXF48ePYKPjw+mTp0KADh69CgOHz6MdevWiZwh0VVUMBJCyCsKCQmp8fjEiRO1lAkhhBB9VlxcDBMTE7HTIDqKCkZCCHlF/v7+VfYpFAqEhYXhwYMHKC0tFSErQgghuur+/fvIzMyEnZ0d6tati8ePH2Pjxo3YvXs37t27J3Z6REdR0xtCCHlF3333nfC4pKQE27dvR1BQEN577z0sXLhQxMwIIYTomg0bNmDFihUwNzdHYWEhfHx84O/vD09PT5w7d07s9IgOo4KREEL+hfz8fAQHB2PLli0YNWoUYmJihI6phBBCiKb89NNPuH79OkxNTZGWlgYLCwvExcWhe/fuYqdGdBx1SSWEkFf01Vdfwc7ODvn5+fjzzz+xfv16KhYJIYRw8cYbb8DU1BQA0K5dO1hYWFCxSLSC5jASQsgrMjAwQIMGDVTWxgIAxpiwoDIhhBCiCc2bN8ekSZMAlH/PhISECNsAEBgYKFZqRMdRwUgIIYQQQkgtt3v37mqPSSQS6sxNuKE5jIQQQgghhNRyFe8mVvT06VOEh4drORuiT2gOIyGEEEIIIa+RZ8+eYceOHRg6dCi6d++OpKQksVMiOozuMBJCCCGEEFLL5ebmIjIyElKpFDdu3MDo0aNx5coV3L9/X+zUiI6jOYyEEEIIIYTUciYmJnBycsLq1avh7OwMAGjfvj3u3LkjcmZE19GQVEIIIYQQQmq5zZs3o169epg8eTIWL16MhIQElQ7dhPBCdxgJIYQQQgh5TSib3EilUpw5cwbe3t4YM2YM+vTpI3ZqREdRwUgIIYQQQshr6PHjxwgLC0NoaChOnDghdjpER9GQVEIIIYQQQmq52NhY4bFy3mKzZs3g7e2N2bNni5UW0QN0h5EQQgghhJBaztHREQkJCVUeq9smRJPoDiMhhBBCCCG1XMV7PJXv99D9H8ITFYyEEEIIIYTUchU7olbujkrdUglPNCSVEEIIIYSQWq5+/fro2rUrGGNITk5G165dAZTfXbx27Rry8/NFzpDoKiOxEyCEEEIIIYTUrGfPnggMDETTpk3FToXoGSoYCSGEEEIIqeU++ugjfPzxx5gzZw4+/fRTGoZKtIaGpBJCCCGEEPIayMnJwbJlyxAXF4eJEyfCwOB/7Ui8vb1FzIzoMrrDSAghhBBCyGvAxMQEjRs3Rn5+PrKyslQKRkJ4oYKREEIIIYSQWi4yMhJLliyBm5sbEhISYGJiInZKRE9QwUgIIYQQQkgtt3XrVkRHR6Njx45ip0L0DM1hJIQQQgghhBCiFg18JoQQQgghhBCiFhWMhBBCCCGEEELUooKREEIIIYQQQohaVDASQgghhBBCCFGLCkZCCCGEEEIIIWpRwUgIIeS11a5dO3Tp0gX29vawt7fHzJkz/9XzLVu2DCUlJRrK7p/btWsXUlJSXunvSiQS2Nraws7ODlZWVti5c6eGsyOEEKKPaFkNQgghr6127dohOjoa1tbWGnk+iUSCvLw8NGjQ4B/9vbKyMgCAgcG/uw7bv39/zJ8/HyNHjvzHf7di7klJSejevTvS0tLQsmXLf5UTIYQQ/UZ3GAkhhOicvLw8TJ8+HT169ICtrS1mzpyJ0tJSAMC6devg5OQEBwcH9OjRA3/++ScACHcnnZ2dYW9vj8ePH2Py5MnYvHmz8Lzz58/HsmXLAJTfjfT09MS4ceNgb2+PBw8e4ObNm/jPf/4DJycn2NnZITg4GABQVFSE8ePHw8rKCnZ2dhgyZEiVnLdt24YLFy5gzpw5sLe3x6FDh6BQKDB//nxYW1vD2toaPj4+f+sOqI2NDRo1aoSMjAxh35o1a9CjRw84OjpixIgRuHfvnvBzfPjhhxg5ciQ6deoEd3d3/PXXXxg4cCA6dOiAefPmCc9x69YtDB48GLa2trC3t8eBAwcAACtWrICPj4/w/+Xn5+Ptt9/GkydPaoxNCCHkNcAIIYSQ15SZmRmzsLBgdnZ2zM7OjkVERDDGGJs+fToLCQlhjDFWVlbGpk6dytatW8cYY+zx48fC3z979izr2rWrsA2A5eXlCduTJk1imzZtErb9/PzY0qVLGWOMLV26lLVq1Yo9evSIMcaYXC5n3bt3Z9euXWOMMVZQUMBsbGzYxYsXWUREBHvvvfeE53n69Knan6dfv34sKipK2A4ODmb9+/dnxcXFrLS0lA0fPpwFBgaq/bsVcz958iSzsrJiL168YIwxtnfvXjZ9+nQml8sZY4yFhISw0aNHCz9Hp06d2PPnz5lcLme2trZsyJAhrLi4mOXn57OmTZuyGzduMMYY69GjB/vpp58YY4ylpKSwt99+m6Wnp7N79+6xpk2bCvG2b9/Oxo0b99LYhBBCaj8jsQtWQggh5N8ICwurMiT1wIEDOHfuHNauXQug/A5f3bp1AQB//fUXvv32Wzx9+hRGRkZITk5GSUmJcPyfGDlyJJo1awYAuHHjBq5evYoJEyYIx/Py8pCcnAxnZ2dcv34d3t7e6NevH0aMGPG3nj8mJgZTp06FsbExAGD69On48ccf4e/vr/b/d3Z2RmFhIe7cuYPQ0FDhZzpw4AAuXLiAbt26AQAUCgUMDQ2Fvzd06FC89dZbACDMgzQ2NoaxsTEsLCyQmpqKli1b4tKlS5g6dSoAwNzcHL1798Z///tffPjhh3BwcMDBgwfh6uqKXbt2YcGCBX8rNiGEkNqNCkZCCCE6hzGGAwcOoEOHDir7S0pK8MEHH+DkyZPo1q0bcnNz8dZbb1VbMBoZGUGhUAjbxcXFKvMbKz5mjMHU1BSXLl1Sm1NycjKOHz+OmJgYLFiwAJcuXULjxo1f+nNIJBKVfZW3Kzpz5gwaNGiAXbt2wcvLCy4uLmjevDkYY/jiiy8wZcoUtX/PxMREeGxoaFhlWy6Xg/1/y4Pq8vHy8sKuXbtgb2+PW7duYfjw4cLPUFNsQgghtRvNYSSEEKJzRo8ejYCAAMjlcgDAs2fPcOvWLRQXF6O0tBRt2rQBAGzatEnl77355pvIyckRtjt27CjMcXz69CkOHTpUbUwLCwvUq1cPISEhwr5bt24hOzsbGRkZkEgkGD16NNasWQPGmNp5fA0bNlSJ/95772HXrl0oKSmBXC7H9u3bMXjw4Jf+/JMnT8agQYOwcuVK4fUIDg5GdnY2AKC0tBR//fXXS5+ncm729vbYvXs3AOD27duIi4uDi4sLAGDs2LE4f/48AgIC4OnpKdxF1ERsQggh4qGCkRBCiM7ZsGEDjIyMYG9vD1tbWwwePBhpaWlo2LAhvv76a/To0QN9+/YVhnoq+fn5YeDAgULTm08++QQPHz6EjY0Npk6dip49e1Yb08jICFFRUZDJZLC1tUXXrl0xbdo0FBUVISkpCc7OzrC1tYWjoyM8PT1ha2tb5TlmzJiBr7/+Wmh6M2PGDNjZ2cHR0RH29vZo164d5syZ87deg9WrV2Pnzp24f/8+PD098fHHH6N///6ws7ODvb09Tpw48c9eVAB79+7Fnj17YGdnhw8++ADbtm0Tim9jY2O4ublh+/bt8PLyEv6OpmITQggRBy2rQQghhBBCCCFELbrDSAghhBBCCCFELSoYCSGEEEIIIYSoRQUjIYQQQgghhBC1qGAkhBBCCCGEEKIWFYyEEEIIIYQQQtSigpEQQgghhBBCiFpUMBJCCCGEEEIIUYsKRkIIIYQQQgghalHBSAghhBBCCCFELSoYCSGEEEIIIYSo9X9xaFC8JEdrrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f393e94940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.98)\n",
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view the details of the corelations above the threshold, we access the `record_collinear` attribute which is a dataframe. The `drop_feature` will be removed and for each feature that will be removed, there may be several correlations it has with the `corr_feature` that are above the `correlation_threshold`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drop_feature</th>\n",
       "      <th>corr_feature</th>\n",
       "      <th>corr_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMT_GOODS_PRICE</td>\n",
       "      <td>AMT_CREDIT</td>\n",
       "      <td>0.987232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FLAG_EMP_PHONE</td>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>-0.999533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YEARS_BUILD_MODE</td>\n",
       "      <td>YEARS_BUILD_AVG</td>\n",
       "      <td>0.992120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COMMONAREA_MODE</td>\n",
       "      <td>COMMONAREA_AVG</td>\n",
       "      <td>0.988074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FLOORSMAX_MODE</td>\n",
       "      <td>FLOORSMAX_AVG</td>\n",
       "      <td>0.984663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       drop_feature     corr_feature  corr_value\n",
       "0   AMT_GOODS_PRICE       AMT_CREDIT    0.987232\n",
       "1    FLAG_EMP_PHONE    DAYS_EMPLOYED   -0.999533\n",
       "2  YEARS_BUILD_MODE  YEARS_BUILD_AVG    0.992120\n",
       "3   COMMONAREA_MODE   COMMONAREA_AVG    0.988074\n",
       "4    FLOORSMAX_MODE    FLOORSMAX_AVG    0.984663"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.record_collinear.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Zero Importance Features\n",
    "\n",
    "This method relies on a machine learning model to identify features to remove. It therefore requires a supervised learning problem with labels. The method works by finding feature importances using a gradient boosting machine implemented in the [LightGBM library](http://lightgbm.readthedocs.io/en/latest/Quick-Start.html). \n",
    "\n",
    "To reduce variance in the calculated feature importances, the model is trained a default 10 times. The model is also by default trained with early stopping using a validation set (15% of the training data) to identify the optimal number of estimators to train. The following parameters can be passed to the `identify_zero_importance` method:\n",
    "\n",
    "* `task`: either `classification` or `regression`. The metric and labels must match with the task\n",
    "* `eval_metric`: the metric used for early stopping (for example `auc` for classification or `l2` for regression). To see a list of available metrics, refer to the [LightGBM docs](http://testlightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters)\n",
    "* `n_iterations`: number of training runs. The feature importances are averaged over the training runs (default = 10)\n",
    "* `early_stopping`: whether to use early stopping when training the model (default = True). [Early stopping](https://en.wikipedia.org/wiki/Early_stopping) stops training estimators (decision trees) when the performance on a validation set no longer decreases for a specified number of estimators (100 by default in this implementation). Early stopping is a form of regularization used to prevent overfitting to training data\n",
    "\n",
    "The data is first one-hot encoded for use in the model. This means that some of the zero importance features may be created from one-hot encoding. To view the one-hot encoded columns, we can access the `one_hot_features` of the `FeatureSelector`.\n",
    "\n",
    "__Note of caution__: in contrast to the other methods, the feature imporances from a model are non-deterministic (have a little randomness). The results of running this method can change each time it is run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[66]\tvalid_0's auc: 0.728334\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[285]\tvalid_0's auc: 0.717859\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[105]\tvalid_0's auc: 0.781248\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[85]\tvalid_0's auc: 0.760535\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[79]\tvalid_0's auc: 0.763826\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[10]\tvalid_0's auc: 0.748422\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[73]\tvalid_0's auc: 0.773226\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[135]\tvalid_0's auc: 0.736587\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[147]\tvalid_0's auc: 0.751588\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[62]\tvalid_0's auc: 0.742976\n",
      "\n",
      "63 features with zero importance after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(task = 'classification', eval_metric = 'auc', \n",
    "                            n_iterations = 10, early_stopping = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 134 one-hot features\n"
     ]
    }
   ],
   "source": [
    "one_hot_features = fs.one_hot_features\n",
    "print('There are %d one-hot features' % len(one_hot_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of methods we have to inspect the results. First we can access the list of features with zero importance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLAG_CONT_MOBILE',\n",
       " 'ORGANIZATION_TYPE_XNA',\n",
       " 'ORGANIZATION_TYPE_Services',\n",
       " 'ORGANIZATION_TYPE_Telecom',\n",
       " 'ORGANIZATION_TYPE_Trade: type 1']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_importance_features = fs.removal_ops['zero_importance']\n",
    "zero_importance_features[10:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Feature Importances\n",
    "\n",
    "The feature importance plot using `plot_feature_importances` will show us the `plot_n` most important features (on a normalized scale where the features sum to 1). It also shows us the cumulative feature importance versus the number of features. \n",
    "\n",
    "When we plot the feature importances, we can pass in a `threshold` which identifies the number of features required to reach a specified cumulative feature importance. For example, `threshold = 0.99` will tell us the number of features needed to account for 99% of the total importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f3922b00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BAQCAlJQUqzz//a0jWtqROGVtv7clwxJGjBiB06dPo02bNpg3bx5atWoFLy8vqFQqXbEwcxfVevrppzFlyhT8+OOPuH79Ojw9PXWjCAApgbnXzZs3AUijZ7RJgjHaP47mpo0BkKoRl0UbR2FhIZ5++mlkZmaiV69eeOWVV9C0aVN4eHhAqVTi1KlTCAsLQ0FBgUXiLk1prYra16v9QiqNOd9zQ58H7WfBwcHBYItBWZ8VHx8fo8+n/YLXtrQCxYmOseOUSiVq1aqFy5cv6x2nVdHW2qlTp+K3335DWFgYFixYgOjoaHh7e+tac+rUqYPLly8b/Z0x9vzapP/e9yknJweA1GprCu3vxc2bN8v8HFjqs2jvOMy4CtE2Wx48eFDWF21Zf/ys1TJjjnguXbqE3bt3o1q1akhMTES3bt3g6+urG+JaVjIgl7e3t27op/ZSQlJSEtLS0hASEoKoqCi9/d3c3AAAr7/+OoTUh8zozdhw8IrSxqBQKKDRaMqMQ3sp6tChQzh37hwaNGiATZs2oWPHjvDy8tJdNqnIe2zJ303t6929e3eZr3Xo0KGyn+dBuHLlitFtWVlZAIpbWoHi167ddj+NRoNr166VOM4c8vPzsXHjRgDAli1b0KdPH90wXkC6HGssLjm08Zs6ZYX2vYmJiSnz94KVcuVhglKF9OjRA25ubsjKytJ98MtD+9+IsT9yD/pDWFo8N2/exNWrV00+l7aWSWhoqMF+LqX1iyitFosptK0kn3/+ud794MGDS+yrbUI/fvx4hZ6zImrVqgVfX18IIXDy5EmTj9PWo4mMjDTYn8HYe2zK+2vJ301beM/N5fz580Zrh2gvcdzb30i7bOznfPr0aRQWFsLR0RHBwcHliqWsn+vly5eRl5cHHx8fhISElNj+999/m7VVtWnTpgCky6em0P5enDx50uKtu1UVE5QqpGbNmpg8eTIAqem0rAJmSUlJOHjwoO6x9pr3H3/8UWLf9PR0o0WbLKW0eNauXVuuc2k792VlZRn8Y7No0aIyj5XbjNu3b1+4urpiz549uHDhgi55NJSg9OzZE05OTkhMTMS///4r6/nMoW/fvgCkgn+m0r5P2j4y91Kr1Vi2bJnB45RKJVQqFTQajdGm/NJ+F27duqWbd0qOfv36AQBWrVoFtVot+zy24O7du9iwYUOJ9ceOHcOhQ4d0xdy0unXrBgBYvXq1wWJ02sKPnTp1KtFBtixlfW6022/cuGHwfS/tMylHz5494ejoiKSkJJOSlLCwMISGhuLKlSsGiwxSxTFBqWLmzp2Ltm3b4vLly2jbti0++eQT3egJrTNnzmDixIno3LmzXhNq9+7dAUjVKxMTE3XrMzIyMGTIkAc+6Zk2nlmzZul96f3444+YP39+uWaKbdq0KTw9PZGeno63335bl6TcvXsXU6ZMwdGjR40eq/1y3Lt3r5yXATc3NzzxxBMoKirC2LFjceXKFbRq1cpgB0N/f39MnToVBQUF6NatW4mKmEII/P777xg/frzRkRzmMHPmTHh6emLdunWYPn26Xr8UALh27RrWrl2LBQsW6Na1bdsWSqUSe/fu1bUSAcD169fxzDPPlNriVdZ73LVrVzg5OeHXX3/F+vXr9c49fPjwUqukluWpp55CVFQUTpw4gd69e5d4X+/evYutW7caHdFkSxwdHTF79mzs379fty4tLQ0jRowAIPWJurfi8ZAhQxAYGIhLly5h5MiRep1KExISdP8IzJgxo9yx+Pr6olq1asjIyDCYbHt7e6NJkybIz8/HSy+9pEtOCwsLsWDBAmzatMlgS5xcgYGBGD9+PIQQ6Nu3L3bt2qW3/dKlS3jzzTf11i1cuBAAMG7cOKxfv77E38Dk5GS8+eabVpsJvNIz+8Blsnm3bt0S/fv319VFcHV1Fc2aNRNRUVF6FSIDAwPF33//rXfs6NGjdduDg4N1lShDQ0N1Y/6N1UG5f71WWfVVjNUtycrKEnXq1BEAhLOzs2jVqpWuUNWMGTPKXQdFW0MCgKhTp46IjIwU7u7uQqFQ6NXLuN/HH3+s29asWTMRExMjYmJidEXUjNVBudfmzZv16o4sXLjQ6L4FBQVi6NCherFGR0eLli1b6iq7AhD//POP0XPcT3tMeWrD7N27V1cxU6VSiebNm4vo6GgRHBysq6w5ZMgQvWOmTp2qe66goCAREREhXFxchJOTk4iPjzdaF2POnDm6QmPh4eG69/jeomna3zPt76723H5+frrjjdVBuX/9/dLT00XLli115w8JCRFt2rQRYWFhQqVSCUCq8GoqU+qgGKpzU1ZNGGO1RQxVkm3cuLEIDw/X1f4ICQkxWFAsKSlJuLu7CwDCzc1NREZG6moXGftcl1WXRmvYsGG6v0GRkZEiJiZGPPLII7rt3377re53qVatWiIyMlLUqlVLABDz5s0z+l6V9h4KIXTViffv36+3Pi8vT/Tq1Uv32gICAnR/F41Vkl22bJmuorC7u7uIiIgQERERwtfXV3eeNWvWlPo+kGFMUKqwffv2idGjR4vGjRsLNzc34eTkJPz9/UXPnj3FunXrRG5uboljCgoKxPz580XDhg2Fk5OTCAgIEBMnThTXr18vs1CbuRMUIaQqrwMGDBCenp7C1dVVhIeH684jp1Dbp59+Klq1aiWcnJxEzZo1xSOPPKKrhmosQRFC+iPVokUL4erqWuLL3pQERa1WC09PTwFAKBQKcf78eaP7am3fvl306dNH1KlTR6hUKuHj4yMiIiLEpEmTxJ49e/TK8pdFToIihBCXL18Wr732mmjRooVwc3MTrq6uIiQkRPTo0UOsXLmyxBdeUVGRePfdd0WTJk2ESqUS3t7eonfv3uL3338vtXCXWq0Ws2fPFo0bN9aVdDf0BbR8+XIRFhYmnJychK+vrxg+fLi4ePFimYXaykpQhJDKoH/wwQeiY8eOwtPTUzg5OYm6deuKjh07ijfffLNcCaG1EpSuXbsKtVot5syZo5t2wt/fX0yYMEFcvXrVaLwpKSli7Nixol69ekKlUgkvLy/x+OOPl6gUrGVqgnLz5k0xefJkUb9+fV2id/9r2759u2jbtq1wcXER7u7uol27duKLL74o9b2Sm6AIIRU7/OSTT0SXLl10P+d69eqJXr16iU8//dTg+f73v/+JUaNGieDgYOHs7Cw8PDxE06ZNxTPPPCM2btxo8G8plU0hBHv3EBHZs127duGxxx5D165dS1y6ILJV7INCRERENocJChEREdkcJihERERkc5igEBERkc1hJ1kiIiKyOWxBISIiIpvD2YwBFBUV4dKlS6hRo0aF51UhIiKqSoQQuHXrFvz9/XUzRZsDExRIJYzr1q1r7TCIiIgqrQsXLuhmLzcHJigonmb7woULcHd3t3I0RERElUdOTg7q1q2r+y41FyYoKJ72293dnQkKERGRDObuIsFOskRERGRzmKAQERGRzWGCQkRERDaHCQoRERHZHCYoREREZHOYoBAREZHNYYJCRERENsfmEpR9+/bhiSeegL+/PxQKBTZv3lzmMXv37kVERARcXFzQoEEDrFq16gFESkRERJZicwnKnTt30LJlS6xYscKk/VNSUtCjRw907NgRR48exWuvvYYXXngBmzZtsnCkREREZCk2V0m2e/fu6N69u8n7r1q1CvXq1cPSpUsBAGFhYTh8+DD+7//+D/379zd4jFqthlqt1j3OycmpWNBEROUlBKDRAGo1kJ+vfzO0rrBQ2l+jAYqKDC+b67E2vntjreg6U7bTgzNrFmDGeXMsweYSlPI6dOgQYmNj9dZ169YN69atQ0FBAVQqVYlj4uLiMG/evAcVIhFVFmo1cPMmkJMD3LoF5OZKt7y84uV7b4bWa9fl5ZWddPDLmaxl/HgmKJaWmZkJX19fvXW+vr4oLCzE1atX4efnV+KYmTNnYtq0abrH2omOiKgSE0JKDLKzgWvXiu9v3JCSDm3ioV2+/3FOjpREWJOTU/HN2bl4WaUCHB0BpbL45uBQ+uOK7HPvnCraZUPrjC3L3ZcenPu+N21RpU9QgJITFIn//isxNnGRs7MznJ2dLR4XEVVAbi5w+TKQmSndX71aIvkounINaceyUaPgGryQDYW5Egw3N6BGDaB6daBaNcDVVbq//1baeheX4iTj3mTDWBLi6Mgva6J7VPoEpU6dOsjMzNRbl5WVBUdHR9SqVctKURGRQXfvSsnGvYmHsftbt8o8nQOA+vevVKmAWrUALy/pvmZNwMOj+ObuXvpyjRpSSwIRWVWlT1Datm2LrVu36q3bsWMHIiMjDfY/ISIL0GiArCzg4kUgPV26v/eWni4lHjdvlu+8Li5AnTpSc3Tt2vqJh5cX7lavhV7DvXANtZB00gvV6taSWj3YEkFU6dlcgnL79m2cPXtW9zglJQXHjh2Dl5cX6tWrh5kzZ+LixYv4+OOPAQDjxo3DihUrMG3aNDz33HM4dOgQ1q1bhy+++MJaL4HIvgghXV45fx5ITZXu09L0k4+MDClJMYWTk5RwaBMPQ/fa5Ro1Sk02NHeAn4f/F2Y9ANUr/GqJyEbYXIJy+PBhdOnSRfdY25l1+PDhSEhIQEZGBtLS0nTbg4ODkZiYiBdffBEffPAB/P39sXz5cqNDjInIgLw84OxZ4PRp6V6biKSmSslIbm7Z53BwAPz8gIAAwzd/fynpqFmTLRxEVCaFEBznlpOTAw8PD9y8eRPu7u7WDofIMjQaKdk4fRo4c0a6aZfvSfoNUiik5CMoCKhfH6hXTxqieG8C4usrdfR8gO7ckfqzAsDt29LVHSJ6sCz1HWpzLShEVEH5+cCpU8Dx49Lt1KnilpH8fOPH1awJNGkCNG4MBAdLyYg2IQkMlEacEBE9IExQiCorIYBLl4DDh4H//a84ITlzRqo6aoiTE9CokZSEaJMR7XKtWrz0QkQ2gwkKUWWRmQkcOSIlJNrbfUPsdTw8gGbNpNtDDxUnI/Xq2dUQ2urVWYyVyF4xQSGyRWo1cPQocOhQ8S09veR+SiXQtCnQqhXQvHlxUhIQwNYQIqrUmKAQ2YKrV4F9+4CkJCkZ+fPPkmXXFQogLAyIjCy+tWwpVS4lIrIzTFCIrCE7G9i7F9izB9i9G/j775L7eHsDbdsW3yIipLogpHP3LvDss9LyJ59Idd2IyD4wQSF6EISQOrBu3SrdfvutZOeJpk2Bjh2LE5KQEF6mKYNGA2zcKC0nJFg1FCIyMyYoRJaSny+1kmiTktRU/e1hYUCXLtKtUyfAx8cqYRIR2SImKETmdPcu8NNPwNdfA9u2ATk5xdtcXICuXYHevYGePaWOrEREZBATFKKKUquBnTulpOT77/WTEl9foFcvKSnp2pWlTomITMQEhUgOjQb45Rfg88+B777Tn6U3MBAYMEC6tWkjzVFDRETlwgSFqDySk6XemB99BFy4ULze319KSJ5+Gnj4YSYlREQVxASFqCwFBcCWLcCqVcCuXcXrPT2BwYOBQYOA9u2ZlBARmRETFCJj0tKANWuAtWuLS8orFEBsLDBqlNSvhIU3rKpaNWkWY+0yEdkPJihE99JogB9/lFpLEhOBoiJpva8vMGYM8Nxz0gy/ZBMUCvY7JrJXTFCIACArS2otWb1aajnR6toVGDcOePJJQKWyXnxERFUMExSq2o4eBZYtA774QiqsBgBeXsDIkcDYsdIMwGSz1Grg+eel5Q8/BJydrRsPEZkPExSqegoLgc2bpcTkwIHi9VFRwOTJ0mgc9i2pFAoLpQFVAPDBB0xQiOxJhRKUzMxMfPvttzh16hRyc3Oxdu1aAMCVK1eQkpKC5s2bw9XV1SyBElXYjRvSJZwVK4qHCDs6SgnJlClSzRIiIrIJshOU+Ph4vPTSS1D/NyW8QqHQJShZWVlo27YtVq1aheeee848kRLJlZ4OLF0qXQPQDvnw9pb6lowbx5LzREQ2SFbhhq1bt2LSpElo3rw5tmzZgvHjx+ttb9q0KVq0aIHNmzebJUgiWY4fB0aMAIKDgXfflZKTZs2A9eulFpQ332RyQkRko2S1oCxevBj16tXD7t27Ub16dRw5cqTEPs2bN8f+/fsrHCBRuQgB7N8PLFoEbN9evL5zZ+BSjwXWAAAgAElEQVSVV4DHH5fGphIRkU2TlaAcO3YMzz77LKqXUoAgICAAly9flh0YUbkIAfz8MzBvXnHHV4UC6N8fmD4diI62bnxERFQushKUoqIiqMqoCXHlyhU4s0s9WZoQwE8/AfPnA4cOSeucnaVLOy+9BDRqZNXwiIhIHlkJSpMmTXDg3uGZ9yksLMTevXvRvHlz2YERlWnnTmDWLOD336XHLi5SUYxXXpEm7yO7V62aVGNPu0xE9kNWJ9khQ4bgzz//xFtvvVVim0ajwcsvv4xz585h2LBhFQ6QqIR//5XmwYmNlZITV1dg2jQgJUUarcPkpMpQKIDataUbuxYR2ReFEEKU96CCggLExsZi3759CAkJgbOzM06cOIH+/fvj8OHDSE1NRWxsLH744QcoKsFfjZycHHh4eODmzZtwd3e3djhkzO3b0sibJUukGYYdHYGJE4HXXgN8fKwdHRFRlWSp71BZLSgqlQo//fQTZsyYgatXr+L48eMQQmDjxo3Izs7Gq6++ii1btlSK5IQqiV27gObNpdE5BQXSaJy//5ZaTJicVFlqtZSjTpwoLROR/ZDVgnIvIQROnz6N7OxsuLu7IywsDEql0lzxPRBsQbFhN25InV3Xr5ceBwVJNc179rRuXGQT7twB3Nyk5du3ObMxkTVY6ju0wnPxKBQKhIaGmiMWIn3ffw+MHw9kZEgdDCZNAhYsKP5GIiIiuyXrEs/JkyexfPlyXLlyxeD2rKwsLF++HP/880+FgqMq6vp1YMgQoE8fKTlp0gTYtw9YvpzJCRFRFSErQXnnnXewcOFC1KpVy+D2WrVqYfHixVi0aFGFgqMq6OefgRYtgM8/B5RKYOZM4NgxoEMHa0dGREQPkKxLPPv370fXrl3h4GA4v1EqlejatSv27dtXoeCoCsnLk5KRZcukx40aAZ98whmGiYiqKFkJSmZmJurWrVvqPgEBAcjIyJAVFFUxf/4JDB0KaC8Jjh8PLF7MHo9ERFWYrEs81atXR5a2fKMRWVlZcHFxkRUUVRGFhVKn1zZtpOSkTh0gMRGIj2dyQkRUxclKUCIiIrB582bcuHHD4Pbr16/ju+++Q+vWrSsUHNmx9HTgkUeA11+XEpV+/aS6Jt27WzsyqkRcXaUCwikp0jIR2Q9ZCcrEiRNx7do1dOnSpUQ/k71796JLly64fv06Jk2aZJYgyc788APQqhWwfz9Qowbw0UfAxo2At7e1I6NKxsEBqF9fuhnpEkdElZSsPii9e/fGyy+/jP/7v/9Dly5d4OzsjDp16iAzMxNqtRpCCEyfPh19+vQxd7xUmRUUALNnAwsXSo/Dw4GvvwZCQqwbFxER2RzZ/3MsWrQI27Ztw+OPPw43Nzekp6fDzc0N3bt3x/bt27FQ+yVEBAAXLgCdOxcnJxMnAgcPMjmhCsnPB6ZPl275+daOhojMqcKl7u0BS91b2M8/AwMHAteuAe7uwLp1wFNPWTsqsgMsdU9kfTY1WSCRSYSQJvPr1k1KTiIigKNHmZwQEVGZKjQXT2FhIU6fPo0bN25Ao9EY3KdTp04VeQqqrPLygHHjgI8/lh4PGwZ8+CHAoedERGQCWQmKEAJz5szB+++/j1u3bpW6r7HEhezYhQvSsOHDh6Vy9e++C7zwgjThHxERkQlkJShvvvkm3n77bdSsWRPDhg1DYGAgHB0rPDGyTnx8PBYvXoyMjAw0bdoUS5cuRceOHY3uv3TpUqxcuRJpaWnw9vbGU089hbi4OBaKs4ZffwWefBLIygJq1ZJG6TzyiLWjIiKiSkZWVrF+/XoEBQXh8OHDRicMlOurr77C1KlTER8fj/bt2+PDDz9E9+7dcfLkSdSrV6/E/p999hlmzJiB9evXo127djhz5gxGjBgBAFiyZIlZY6MybNsGPP20dHmnZUtg82apQAUREVE5yeoke/nyZfTp08fsyQkAvPfeexg9ejTGjBmDsLAwLF26FHXr1sXKlSsN7n/o0CG0b98ezzzzDOrXr4/Y2FgMHjwYhw8fNntsVIq1a6WWk7w8qRrsgQNMToiISDZZCUpwcDBycnLMHQvy8/Nx5MgRxMbG6q2PjY3FwYMHDR7ToUMHHDlyBL///jsA4Ny5c0hMTETPnj2NPo9arUZOTo7ejWQSAnjzTeC554CiImDkSOD774vHfhJZkKsrcPy4dGOpeyL7IitBmTRpErZt21bmhIHldfXqVWg0Gvj6+uqt9/X1RWZmpsFjBg0ahDfffBMdOnSASqVCw4YN0aVLF8yYMcPo88TFxcHDw0N3K2tmZjJCCGDmTGDOHOnx669LNU5UKuvGRVWGgwPQtKl0Y6l7Ivsiqw9Kr169sGfPHrRr1w5z5sxBeHg4PDw8DO5rqN9IWRT3jfYQQpRYp7Vnzx68/fbbiI+PR5s2bXD27FlMmTIFfn5+mD17tsFjZs6ciWnTpuke5+TkMEkpLyGAl14CtP18liwBpk61bkxERGQ3ZCUo9evXh0KhgBACI0eONLqfQqFAYWGhyef19vaGUqks0VqSlZVVolVFa/bs2Xj22WcxZswYAEDz5s1x584djB07Fq+//jocDPxb5ezsDGdnZ5PjovsUFUnDhj/4QHocHw+MH2/dmKhKys8HFiyQll97DXBysm48RGQ+shKUYcOGGW3RqAgnJydERERg586d6Nu3r279zp078eSTTxo8Jjc3t0QSolQqIYQAq/hbgBDAlClScqJQAGvWAKNHWzsqqqIKCoB586Tl6dOZoBDZE1kJSkJCgpnDKDZt2jQ8++yziIyMRNu2bbF69WqkpaVh3LhxAKTkKCAgAHFxcQCAJ554Au+99x7Cw8N1l3hmz56N3r17Q6lUWizOKmvWLGDFCik52bABGD7c2hEREZEdMl91NTMZOHAgrl27hvnz5yMjIwPNmjVDYmIigoKCAABpaWl6LSazZs2CQqHArFmzcPHiRdSuXRtPPPEE3n77bWu9BPu1cGFxe3p8PJMTIiKyGM5mDM5mbJKVK4EJE6TlhQuBV16xbjxE4GzGRLbAUt+hsltQbt26hRUrVmDXrl24dOkS1Gp1iX0UCgWSk5MrFCDZgE8/BSZOlJZfe43JCRERWZysBOXKlSto164dkpOT4e7ursue8vPzkZeXBwDw9/eHivUwKr/Nm4ERI6TOsZMmAW+9Ze2IiIioCpBV2mju3LlITk7Gxx9/jOvXrwMAXnzxRdy5cwe//fYboqOjUb9+fZw4ccKswdIDtmsXMHAgoNEAw4YBy5ZxRmIiInogZCUoiYmJ6Nq1K4YOHVpiuHFUVBR++OEHpKamYu7cueaIkazh0CFpbp38fKBfP6lCLEt1ko1xcQF+/126cfJyIvsi6xsnIyMD4eHhusdKpVJ3aQcAPD090b17d3zzzTcVj5AevGPHgB49gNxcIDYW+PxzwNHmBnwRQakEoqKkG6sKENkXWQmKh4cHCgoKdI89PT2Rnp6ut4+7uzsuX75csejowTtzRkpKbtwA2rcHvv0WYNVdIiJ6wGQlKA0aNEBqaqrucXh4OHbu3Ins7GwAQF5eHrZu3SprHh6yokuXpOTkyhUgPBzYvp3jNsmm5ecDixdLt/x8a0dDROYkK0GJjY3Fzz//jNzcXADA888/j6ysLLRs2RIDBgxAs2bNkJycjBEjRpgzVrKkGzeAxx8Hzp8HQkKAH38EjEwASWQrCgqkUe+vvCItE5H9kJWgjBs3DmvWrNElKP369cPixYtx+/ZtbNq0CZmZmZg2bRqmT59u1mDJQvLygN69gb//BurUAXbsAHx8rB0VERFVYWatJKvRaHD16lX4+PhYZDJBS6nSlWQ1GmDAAOC77wB3d2DfPqBlS2tHRWQSVpIlsj5LfYfKakHZt28f0tLSSqxXKpXw9fWFQqFAeno69u3bV+EAycJmzpSSEycn4PvvmZwQEZFNkJWgdOnSpcwZjT/77DN06dJFzunpQVm/XupdCAAJCUDnztaMhoiISEdWgmLKVaGioqJKdZmnytmzB3j+eWl5zhxg8GCrhkNERHQvi5UG/ffff+HBUSC26exZoH9/oLBQKmXPir9ERGRjTC4POmrUKL3Hmzdv1quFoqXRaHT9Tx5//PEKB0hmlpsrla7Pzgaio4ENGzi/DlVaLi7A7t3Fy0RkP0wexeNwzzwsCoWi1Ms8CoUCUVFR+PTTTxESElLxKC2syoziEUKamfjjjwFfX+DoUcDPz9pRERFRJWap71CTW1BSUlIASP1PGjRogKlTp2LKlCkl9lMqlfD09ER1jvezPWvXSsmJgwPw5ZdMToiIyGaZnKAEBQXpljds2IDw8HC9dWTj/vwTmDxZWn77bY7YIbtQUACsXi0tjx0LqFTWjYeIzEdWoTYHBwcMHjwYn332mSVieuDs/hJPTg7QqhWQkgI88QSwebPUikJUybFQG5H12VShNg8PD9StW9dsQZCFTZ8uJSdBQcBHHzE5ISIimyfrmyo6Ohp//fWXuWMhS9ixo7gNPCEB8PS0ajhERESmkJWgzJs3D7/88gs++ugjc8dD5nTzJjBmjLQ8aRL7nRARUaVhcifZe+3YsQOdO3fGqFGj8P777yM6Olo3B8+9FAoFZs+ebZZASYaXXwYuXAAaNADeecfa0RAREZlMdidZk06uUECj0ZQ7qAfNLjvJ7tgBdOsmLe/ZA8TEWDUcIktgJ1ki67N6HZR77daWbiTbpFYDEyZIy5MnMzkhIqJKR1aCEsMvPNu2dCmQnCwVYnv7bWtHQ2Qxzs7Atm3Fy0RkP2QlKGTDMjKAt96Slt95B6hRw7rxEFmQoyPQs6e1oyAiS6hQgnLw4EEkJCTg2LFjumtP4eHhGDZsGDp06GCuGKk8Zs6ULsa3aQMMHWrtaIiIiGSR1UkWAF5++WUsWbJEN2mgg4MDioqKpJMqFJgyZQree+8980VqQXbTSfa334CHHy5ejo62bjxEFlZQAGgLWg8ZwlL3RNZgU5VkP/74Y7z33nto0qQJvvjiC2RkZKCwsBCZmZn48ssvERoaimXLluHjjz82W6BUhqIi4IUXpOXhw5mcUJWQnw+MHCnd8vOtHQ0RmZOsFpS2bdvi0qVLOH78OGoY6OOQk5OD5s2bw8/PD7/++qtZArUku2hB0Q4rdnMDzpzhTMVUJXCYMZH12VQLyvHjx9G/f3+DyQkAuLu7o1+/fjhx4kSFgqNy0LZWDR/O5ISIiCo92bPGldXwcn9VWbKgW7eAb7+Vlp991rqxEBERmYGsBKVZs2bYtGkTbt++bXD7rVu3sGnTJjRt2rRCwZGJvv0WyMsDGjVi3xMiIrILshKUcePGIT09HW3btsWmTZtw9epVAMDVq1exceNGtGvXDunp6Rg/frxZgyUjtJd3hg0D2HJFRER2QFYdlOHDh+PYsWNYtmwZnn76aQD6w4yFEJg8eTKGDx9uvkjJsCtXAO3UA0OGWDcWIiIiM5FdqG3JkiXo378/NmzYgGPHjiEnJ0dXqG348OHo2LGjOeMkY7ZvB4QAWrUCgoOtHQ3RA+XsDHz9dfEyEdmPClWS7dChAyvGWtvWrdL9E09YNw4iK3B0BAYMsHYURGQJskfxkA1Qq6X6JwATFCIisisVakFJSkrCRx99pJuLx8PDA61ateJcPA/K3r1Sdao6dYCICGtHQ/TAFRYC330nLfftK7WoEJF9kPVxFkJgwoQJWL16dYm5eA4fPox169Zh7NixiI+PZz0US9Je3unVC3BgYxhVPWo18F8/fdy+zQSFyJ7I+lZ799138eGHH6JZs2b45ptvkJmZqZuL5+uvv0bTpk2xevXqSjNZYKUkBLBtm7TMyztERGRnZM3F07hxY2g0Gvz999+oVq1aie23b99GixYt4OjoiDNnzpglUEuqlHPxnDoFhIUBTk5AdjYnIaEqiXPxEFmfTc3Fc+HCBfTr189gcgIAbm5u6NevHy5cuFCh4KgUiYnSfUwM/yoTEZHdkZWgBAYG4u7du6Xuo1arERgYKCuo+Ph4BAcHw8XFBREREdi/f3+p+9+4cQMTJ06En58fXFxcEBYWhkTtF7i9+uEH6b57d+vGQUREZAGyEpRRo0bh66+/xuXLlw1uz8jIwFdffYUxY8aU+9xfffUVpk6ditdffx1Hjx5Fx44d0b17d6SlpRncPz8/H4899hhSU1OxceNGnD59GmvWrEFAQEC5n7vSuH0b2LdPWu7Rw7qxEBERWYCsPigpKSmYMmUKDh8+jClTpqBDhw7w8fFBVlYW9u/fj+XLlyMqKgpLly6FUqnUO7ZevXqlnrtNmzZo3bo1Vq5cqVsXFhaGPn36IC4ursT+q1atwuLFi3Hq1CmoVKryvhQAlbAPypYtwJNPAg0aAGfPcv4dqrLYB4XI+iz1HSprUF7Dhg2hUCgghMBrr71WYrsQAtu2bcM27SiT/ygUChQWFho9b35+Po4cOYIZM2borY+NjcXBgwcNHrNlyxa0bdsWEydOxPfff4/atWvjmWeewauvvloiOdJSq9VQq9W6xzk5OUZjsknay1fduzM5oSrNyQnYsKF4mYjsh6wEZdiwYRapb3L16lVoNBr4+vrqrff19UVmZqbBY86dO4dffvkFQ4YMQWJiIv79919MnDgRhYWFmDNnjsFj4uLiMG/ePLPH/0AIUdz/hJd3qIpTqYARI6wdBRFZgqwEJSEhwcxh6Ls/+RFCGE2IioqK4OPjg9WrV0OpVCIiIgKXLl3C4sWLjSYoM2fOxLRp03SPc3JyULduXfO9AEs6eRJIS5NmRuvc2drREBERWYRN1V309vaGUqks0VqSlZVVolVFy8/PDyqVSu9yTlhYGDIzM5Gfnw8nA+2+zs7OcK6sU59qL+906QIYGeZNVFUUFgI//SQtd+vGSrJE9sSm6qM7OTkhIiICO3fu1Fu/c+dOtGvXzuAx7du3x9mzZ1FUVKRbd+bMGfj5+RlMTio9Di8m0lGrpZkeevWSlonIfshOUA4ePIi+ffuiQYMGcHZ2hlKpLHFzlPHvzLRp07B27VqsX78e//zzD1588UWkpaVh3LhxAKT+LzNnztTtP378eFy7dg1TpkzBmTNnsH37dixYsAATJ06U+9JsV04OoK0Jw/4nRERkx2Q1iH766acYPnw4hBBo0KABoqOjZSUjhgwcOBDXrl3D/PnzkZGRgWbNmiExMRFBQUEAgLS0NDjcMzFe3bp1sWPHDrz44oto0aIFAgICMGXKFLz66qtmicem7N4ttWmHhEg3IiIiOyWrDkqTJk1w7do1/PDDD4iKirJEXA9UpamD8sorwOLFwHPPAatXWzsaIqtjHRQi67OpuXjS0tIwaNAgu0hOKpWkJOm+fXvrxkFERGRhshKU+vXrIz8/39yxUGnu3gUOH5aWmaAQEZGdk5WgjBs3Dtu2bUN2dra54yFj/vwTyM8HfHyAhg2tHQ0REZFFyerZOmXKFJw9exbt27fHrFmz0LJlS6PXncqae4dMpL28064dy9sT/cfJCVixoniZiOyH7KE3rVq1wqeffophw4YZ3aesuXeoHNj/hKgElQqwx4oCRCQzQXn//fcxdepUqFQqdOnSBX5+fmYbZkwGCAFoJ0tkgkJERFWArKxiyZIlCAgIwMGDBxEYGGjumOh+Z88CV65I8++0bm3taIhshkZTXLuwY0fAyATmRFQJyUpQMjMz8fzzzzM5eVC0l3ciI6UkhYgASIPbunSRllkHhci+yBrFExISghs3bpg7FjKG/U+IiKiKkZWgvPjii/j+++9x/vx5c8dDhrD/CRERVTGyLvE0bNgQMTExiIyMxJQpU9CqVSujw4w7depUoQCrvOxs4ORJadnIjM5ERET2RlaC0rlzZygUCgghMGfOHChKqcuh0WhkB0cADh2S7hs3Bry9rRsLERHRAyIrQSkrKSEz4uUdIiKqgmQlKHPnzjVzGGQUO8gSEVEVxOpqtqywEPjjD2m5bVvrxkJkg1QqYNGi4mUish9MUGzZyZNAbi7g7g6Ehlo7GiKb4+QETJ9u7SiIyBJMTlAeeuihcp9coVDgxIkT5T6O/vP779J9VBTgIGtEOBERUaVkcoJy6tQpS8ZBhmgTlOho68ZBZKM0GuDPP6Xl1q1Z6p7InpicoBQVFVkyDjKECQpRqe7eLf54sNQ9kX3hdQNbdecOcPy4tMwEhYiIqhgmKLbqzz+l9uuAAMDf39rREBERPVBMUGwVL+8QEVEVxgTFVjFBISKiKowJiq1igkJERFUYExRblJUFpKYCCgUQGWntaIiIiB44VpK1Rdry9mFhUhVZIjJIpQLeeKN4mYjsBxMUW8TLO0QmcXICOHcpkX2SfYmnsLAQS5YsQXR0NNzd3eHoWJzrHDt2DBMmTMCZM2fMEmSVwwSFiIiqOFktKHl5eYiNjcXBgwfh7e0Nd3d33LlzR7c9ODgYGzZsgJeXF9566y2zBVslCMEEhchERUXAP/9Iy2FhnLKKyJ7I+jgvWLAASUlJiIuLQ2ZmJsaMGaO33cPDAzExMfjpp5/MEmSVkpwMZGcDzs5A8+bWjobIpuXlAc2aSbe8PGtHQ0TmJCtB+eqrr9C5c2e88sorUCgUUCgUJfZp0KAB0tLSKhxglaNtPQkPly6wExERVUGyEpS0tDRERUWVuo+7uztu3rwpK6gq7dgx6T4iwrpxEBERWZGsBKVGjRq4cuVKqfskJyejdu3asoKq0v7+W7rn5R0iIqrCZCUoDz/8MLZu3Wq0hSQ9PR2JiYno1KlThYKrkrQJSosW1o2DiIjIimQlKNOnT0d2djYeffRRHDx4EIWFhQCA3Nxc/Pzzz4iNjUVBQQGmTZtm1mDtXnY2cPGitNysmXVjISIisiJZw4w7deqEDz74AC+88AI6duyoW1+jRg0AgFKpRHx8PCLYj6J8tK0nwcHAf+8lERFRVSS7kuy4ceMQExODVatW4bfffkN2djbc3d3Rpk0bTJgwAU2bNjVnnFXD//4n3bP/CZFJVCrg5ZeLl4nIflSo1H1YWBiWLVtmrliIHWSJysXJCVi82NpREJElyOqDkpSUZO44CChuQWEHWSIiquJkJSgdO3ZESEgI5s6di3///dfcMVVNRUXA8ePSMltQiExSVASkpkq3oiJrR0NE5iQrQRk9ejSys7Mxf/58hIaGom3btoiPj8e1a9fMHV/VkZIC3Lkjlbhv1Mja0RBVCnl5Up/y4GCWuieyN7ISlDVr1iAzMxPffPMNevfujaNHj2LSpEnw9/fHk08+iY0bN0KtVps7Vvum7X/y0EOAY4W6BhEREVV6suf+dHJyQv/+/fHdd98hIyMD8fHxiIqKwrZt2zBw4EDUqVMHzz33nDljtW8cwUNERKRjlsnJPT09MW7cOBw4cADJycmYMWMGcnNzsX79enOcvmpgBVkiIiIdsyQoWr/88gvmz5+PDz74AAUFBQZnOTZVfHw8goOD4eLigoiICOzfv9+k47788ksoFAr06dNH9nNbBYcYExER6VQ4Qfnrr78wffp0BAYG4rHHHkNCQgL8/Pzw1ltv4dy5c7LO+dVXX2Hq1Kl4/fXXcfToUXTs2BHdu3dHWlpaqcedP38eL7/8sl5120ohLw/QjoZiCwoREREUQghR3oPS09Px2Wef4dNPP8XJkychhICPjw8GDRqEoUOHIjIyskJBtWnTBq1bt8bKlSt168LCwtCnTx/ExcUZPEaj0SAmJgYjR47E/v37cePGDWzevNmk58vJyYGHhwdu3rwJd3f3CsUuy5EjQGQk4O0NZGUBFWh5IqpK7twB3Nyk5du3gerVrRsPUVVkqe9QWcNFgoKCAAAuLi54+umn8eyzz6Jbt25QKpUVDig/Px9HjhzBjBkz9NbHxsbi4MGDRo+bP38+ateujdGjR5d5OUitVuuNMsrJyalY0BV1bwdZJidEJnN0BCZMKF4mIvsh6yMdExODYcOG4amnnoKb9t8XM7l69So0Gg18fX311vv6+iIzM9PgMUlJSVi3bh2OHTtm0nPExcVh3rx5FY7VbNhBlkgWZ2fggw+sHQURWYKsBOWXX34xdxwl3N/BVghhsNPtrVu3MHToUKxZswbe3t4mnXvmzJmYNm2a7nFOTg7q1q1bsYArgkOMiYiI9Nhco6i3tzeUSmWJ1pKsrKwSrSoAkJycjNTUVDzxxBO6dUX/1bx2dHTE6dOn0bBhQ71jnJ2d4ezsbIHoZeIIHiJZhACuXpWWvb15hZTInpiUoIwaNQoKhQILFiyAr68vRo0aZdLJFQoF1q1bV66AnJycEBERgZ07d6Jv37669Tt37sSTTz5ZYv/Q0FD8rf2C/8+sWbNw69YtLFu2zLotI6bIypJuANC0qXVjIapkcnMBHx9pmZ1kieyLSQlKQkICFAoFXn31Vfj6+iIhIcGkk8tJUABg2rRpePbZZxEZGYm2bdti9erVSEtLw7hx4wAAw4YNQ0BAAOLi4uDi4oJmzZrpHV+zZk0AKLHeJp04Id03aMC/rkRERP8xKUFJSUkBAAQEBOg9tpSBAwfi2rVrmD9/PjIyMtCsWTMkJibqRg+lpaXBwcGsNeasR5ugsPWEiIhIR1YdFHtj1Too48YBH34IzJwJLFjwYJ+bqJJjHRQi67PUd6isZoj58+dj3759pe6TlJSE+fPnywqqStG2oFSGy1FEREQPiKwEZe7cudizZ0+p+xw4cMC2ao3YIiGA48elZV7iISIi0rFYR478/Hz76SdiKRkZwI0bgFIJNGli7WiIiIhshuw6KKXNVJyfn4/9+/cbrFtC99C2noSEAC4u1o2FqBJydASGDy9eJiL7YfJHukGDBnqPlyxZgg0bNpTYT6PR4OrVq7h79y6ee+65ikdoz9j/hKhCnJ0BE6seEFElY3KCUlRUpGs1USgUEELA0AAglUqFpk2b4pFHHsHs2bPNF6k9Yv8TIiIig0xOUFJTU3XLDk0i91cAACAASURBVA4OePHFFzFnzhxLxFR1sAWFqEKEkKrJAkC1aix1T2RPZF21TUlJ0VVrJZmEYIJCVEG5uayDQmSvZCUo2oquVAFpadJfVJVK6iRLREREOhXq937o0CHs2rULly5dglqtLrFd7lw8VYK2/0loqJSkEBERkY6sBKWwsBCDBw/Gt99+CyGErtOslvYxE5RScA4eIiIio2RVUnv33XexadMmjBw5EocPH4YQAlOnTsWhQ4ewcOFC1KxZEwMGDEBycrK547Uf2hYU9j8hIiIqQVYLymeffYZmzZph7dq1unU1a9ZEmzZt0KZNG/To0QPR0dF45JFH8Pzzz5stWLvCIcZERERGyWpBOXv2LDp37qx7rFAoUFBQoHvctGlTPPHEE1i5cmWFA7RLGg3wzz/SMltQiIiISpDVguLk5IRq1arpHru5uSErK0tvn6CgIGzdurVi0dmrc+eAu3cBV1cgONja0RBVWkol8NRTxctEZD9kJSh169bFhQsXdI9DQ0Oxb98+XcdYAPj111/h5eVlnijtjbaDbFgY/6oSVYCLC/DNN9aOgogsQdYlnpiYGF1CAgADBw7E6dOn0atXL3zwwQcYPHgwDhw4gMcff9yswdoN9j8hIiIqlawWlFGjRkGj0SA9PR1169bF5MmTsWfPHmzbtg0//PADACA6OhrvvPOOWYO1G6dOSfcPPWTdOIiIiGyUrASldevWeh1gVSoVtmzZgsOHDyM5ORlBQUGIjo6Gg4OsBhr7d/q0dN+kiXXjIKrk7txhqXsie1WhSrL3i4yMRGRkpDlPaX+EYIJCRERUBjZxPGgZGcCtW1Ln2IYNrR0NERGRTTKpBWXUqFGyTs5S9wZoW0+CgwFnZ+vGQkREZKNMSlASEhJknZwJigHaDrK8vENERGSUSQlKSkqKpeOoOrQtKKGh1o2DiIjIhpmUoAQFBVk6jqqDHWSJiIjKZNZRPGQCXuIhMhulEujRo3iZiOyHrARl3759Ju/bqVMnOU9hn/LygPPnpWUmKEQV5uICbN9u7SiIyBJkJSidO3fWzblTFo1GI+cp7NPZs1IdlJo1AR8fa0dDRERks2QlKHPmzDGYoNy8eRN//vkn9u3bh549e7Jo2/20/U8aNwZMTPCIiIiqIlkJyty5c0vdvnHjRowYMQLz5s2Tc3r7dfasdN+4sXXjILITd+4UN0ZmZbHUPZE9sUgl2aeeegpdunTBzJkzLXH6yuvff6X7kBDrxkFkR3JzpRsR2ReLlboPCwvDoUOHLHX6yknbgtKokXXjICIisnEWS1COHj3K2YzvxxYUIiIik8jqg5KWlmZwfWFhIS5evIiEhAT88ssvePLJJysUnF25c0eaKBBgCwoREVEZZCUo9evXL3WYsRACwcHBWLJkiezA7E5ysnTv5QV4elo3FiIiIhsnK0EZNmyYwQTFwcEBnp6eiIyMRJ8+feDi4lLhAO0GL+8QERGZTFaCInd24yqNHWSJzM7BAYiJKV4mIvvBuXgeFLagEJmdqyuwZ4+1oyAiS6hwglJUVITLly+joKDA4PZ69epV9CnsA1tQiIiITCY7Qfniiy+waNEinDhxwuh8OwqFAoWFhbKDsytsQSEiIjKZrATl3XffxSuvvAKVSoVOnTrBz88Pjo68WmRUbi5w6ZK0zBYUIrO5cweoX19aTk1lqXsieyIrq1i+fDkCAgJw8OBBBAYGmjsm+5OaKt27u0vDjInIbK5etXYERGQJsvq9X7lyBf3792dyYqrz56V77b96REREVCpZCUpoaCiuX79u7ljslzZBCQqybhxERESVhKwE5aWXXsL333+P89ovXguIj49HcHAwXFxcEBERgf379xvdd82aNejYsSM8PT3h6emJRx99FL///rvFYis3JihERETlIqsPypAhQ5CZmYl27dphwoQJaNmyJdzd3Q3u26lTp3Kf/6uvvsLUqVMRHx+P9u3b48MPP0T37t1x8uRJg8OW9+zZg8GDB6Ndu3ZwcXHBokWLEBsbixMnTiAgIKDcz292TFCIiIjKRSGEEHIOnD17NpYsWYK8vLxS9zM2BLk0bdq0QevWrbFy5UrdurCwMPTp0wdxcXFlHq/RaODp6YkVK1Zg2LBhZe6fk5MDDw8P3Lx502iiVSHt2gGHDgFffw0MGGD+8xNVUXfuAG5u0vLt2xzFQ2QNlvoOldWCMmfOHCxYsAC1a9fGoEGDzDrMOD8/H0eOHMGMGTP01sfGxuLgwYMmnSM3NxcFBQXwMjJiRq1WQ61W6x7n5OTID9gUbEEhsggHByAysniZiOyHrKxi/fr1aNy4Mf744w+4af99MZOrV69Co9HA19dXb72vry8yMzNNOseMGTMQEBCARx991OD2uLg4zJs3r8KxmiQ/H8jIkJY5iofIrFxdgT/+v717j4uqTv8A/hluAwqOEAiCBkheUkwFFMG7FmqKlJtiuahd3LwmP7t6C7IU07ZX25bdo7RW3JJsW8kVFLwsaHmJFMq8gGMKUqSAoFyf3x/sjI4z6DDMwICf9+t1Xo3f8z3nPN/HcebpnPM9831LR0FElmDS/3NcvHgREyZMMHtxcr0bfy1ZRAz+gvKN1q5di02bNiE5ObnBX1NesmQJSkpKtMvZs2fNErNBZ88CIvWfpB4eljsOERFRG2LSGZS+ffuiQHNWwMzc3d1ha2urd7akqKhI76zKjV577TWsXr0aaWlpuOeeexrsp1QqoVQqzRLvLWku79x5J2BEgUVEREQmnkFZtmwZtm7disOHD5s7Hjg4OCA4OBipqak67ampqQgPD29wu3Xr1uHll1/G9u3bEaK5KG0NNE+R5f0nRGZXUVF/5dTPr/41EbUdJp1BuXjxIu677z6Eh4fjz3/+M/r379/gnbvGzKK50eLFixETE4OQkBCEhYXh/fffh1qtxpw5c7T79PHx0c7oWbt2LVasWIF//OMf8PPz0559cXZ2tuhlKKPwBlkiixG59k/MtPmIRGStTCpQZs2aBYVCARHBxx9/DKDhe0ZMKVCio6NRXFyMlStXoqCgAIGBgUhJSYHv/77k1Wo1bK67ZX/9+vWoqqrCQw89pLOfuLg4xMfHN/r4ZsXH3BMRETWaSQVKYmKiuePQM2/ePMybN8/guoyMDJ0/52suo1gjnkEhIiJqNJMKlJkzZ5o7jrZLM0PIwBNwiYiIyDA+2sjSNL8F36lTy8ZBRETUiph0BkWtVhvd19Bv59w2qquBkpL61+7uLRsLERFRK2JSgeLn52fUQ9MUCgVqampMOUTboDl7YmMDdOzYsrEQtUEKBdC797XXRNR2mFSgzJgxw2CBUlJSguzsbOTl5WHEiBHwu91nrmgKFDc3wNa2ZWMhaoPatQNyclo6CiKyBJMKlE8++aTBdSKCv/71r1i7di0++ugjU+NqGzQFCi/vEBERNYrZb5JVKBR45pln0KdPHzz77LPm3n3rwgKFiIjIJBabxRMSEoJdu3ZZavetAwsUIouqqAD69Klf+Kh7orbFpEs8xjh16tTtfYMscK1A4a8YE1mECJCbe+01EbUdZi1Q6urqcO7cOXzyySf4+uuvMWbMGHPuvvXhGRQiIiKTmFSg2NjY3HSasYigY8eOWLduncmBtQksUIiIiExiUoEyfPhwgwWKjY0NXF1dERISgkcffRSenp5NDrBVY4FCRERkEpMKlBt/rI8awAKFiIjIJPwtHktigUJERGSSRhUoq1atwtKlS1FdXd1gn6qqKixduhRr1qxpcnCtHgsUIotSKABf3/qFj7onaluMLlDS0tLw4osv4o477oC9vX2D/RwcHODu7o5ly5bd3s9Bqai49mAGFihEFtGuHZCfX7+0a9fS0RCRORldoGzYsAGurq5YsGDBLfvOnz8fbm5uSExMbFJwrVpxcf1/7e0BF5eWjYWIiKiVMbpAyczMxL333gulUnnLvkqlEvfeey8yMzObFFyrdv3lHZ57JiIiahSjC5Tz58+jW7duRu/Y398fBQUFJgXVJvD+EyKLu3IFGDiwfrlypaWjISJzMnqasY2NzU1vjr1RdXU1bGxu40lCLFCILK6uDjh48NprImo7jK4gvL29cezYMaN3fOzYMfj4+JgUVJvAAoWIiMhkRhcow4YNw65du5Cfn3/Lvvn5+di1axeGDx/elNhat99+q/8vCxQiIqJGM7pAmT9/Pqqrq/HQQw/hd83ZAQOKi4sxZcoU1NTUYO7cuWYJslXiGRQiIiKTGX0PSlBQEGJjY/HGG2+gd+/emDNnDkaNGoUuXboAAM6dO4edO3fi/fffx2+//YbFixcjKCjIYoFbPRYoREREJlOIiBjbWUSwbNkyrFu3DnUG7kgTEdja2uK5557DK6+8ctNfPLYmpaWlUKlUKCkpQYcOHcyz06Ii4Px5wMurfiEisysvB5yd619fvgy0b9+y8RDdjizyHYpGFigap06dQmJiIjIzM1FYWAgA8PLywpAhQzBr1iwEBASYLcDmYKnkEpFllZcDfn71r/PzWaAQtQSrKlDaGhYoREREprHUd+ht/KASIiIislYsUIiIiMjqsEAholbryhVg5Mj6hY+6J2pbjJ5mTERkberqgN27r70moraDZ1CIiIjI6rBAISIiIqvDAoWIiIisDgsUIiIisjosUIiIiMjqcBYPEbVq7dq1dAREZAksUIio1Wrfvv73eIio7eElHiIiIrI6LFCIiIjI6rBAIaJW6+pVYMKE+uXq1ZaOhojMifegEFGrVVsLpKRce01EbQfPoBAREZHVYYFCREREVsdqC5T169fD398fjo6OCA4Oxt69e2/af8uWLejduzeUSiV69+6Nr776qpkiJSIiInOzygJl8+bNiI2NxbJly3DkyBEMGzYM48ePh1qtNtg/KysL0dHRiImJQXZ2NmJiYjB16lQcOHCgmSMnIiIic1CIiLR0EDcKDQ1FUFAQ3nnnHW3b3XffjQceeAAJCQl6/aOjo1FaWopvv/1W2zZu3Di4urpi06ZNtzxeaWkpVCoVSkpK0KFDB/MMgogsrrwccHauf335cv2D24ioeVnqO9TqzqBUVVXh0KFDiIiI0GmPiIhAZmamwW2ysrL0+o8dO7bB/pWVlSgtLdVZiIiIyHpYXYHy+++/o7a2Fp6enjrtnp6eKCwsNLhNYWFho/onJCRApVJpl65du5oneCJqVu3bAyL1C8+eELUtVlegaCgUCp0/i4hem6n9lyxZgpKSEu1y9uzZpgdMREREZmN1D2pzd3eHra2t3tmPoqIivbMkGl5eXo3qr1QqoVQqzRMwERERmZ3VnUFxcHBAcHAwUlNTddpTU1MRHh5ucJuwsDC9/jt27GiwPxEREVk3qzuDAgCLFy9GTEwMQkJCEBYWhvfffx9qtRpz5swBAMyYMQM+Pj7aGT2LFi3C8OHD8eqrryIqKgpff/010tLSsG/fvpYcBhEREZnIKguU6OhoFBcXY+XKlSgoKEBgYCBSUlLg6+sLAFCr1bCxuXbyJzw8HElJSVi+fDlWrFiBgIAAbN68GaGhoS01BCIiImoCq3wOSnPjc1CIiIhMc9s8B4WIiIiIBQoRERFZHRYoREREZHVYoBAREZHVYYFCREREVocFChEREVkdq3wOSnPTzLTmrxoTERE1jua709xPLWGBAqCsrAwA+KvGREREJiorK4NKpTLb/vigNgB1dXU4f/48XFxcbvqLycYqLS1F165dcfbsWT74zQKYX8tjji2PObY85tjyNDnOzc1Fz549dZ7y3lQ8gwLAxsYGXbp0Mft+O3TowH8UFsT8Wh5zbHnMseUxx5bn4+Nj1uIE4E2yREREZIVYoBAREZHVsY2Pj49v6SDaIltbW4wcORJ2dryKZgnMr+Uxx5bHHFsec2x5lsoxb5IlIiIiq8NLPERERGR1WKAQERGR1WGBQkRERFaHBQoRERFZHRYoZrZ+/Xr4+/vD0dERwcHB2Lt3b0uH1GrFx8dDoVDoLF5eXtr1IoL4+Hh4e3vDyckJI0eORE5OTgtGbP327NmDyMhIeHt7Q6FQYOvWrTrrjcnpxYsXERMTA5VKBZVKhZiYGFy6dKk5h2G1bpXfWbNm6b2nBw8erNOnsrISCxcuhLu7O9q3b49Jkybh119/bc5hWLWEhAQMHDgQLi4u6NSpEx544AEcP35cp48xOVSr1YiMjET79u3h7u6Op556ClVVVc05FKtkTH5Hjhyp9z6eNm2aTh9zfE6wQDGjzZs3IzY2FsuWLcORI0cwbNgwjB8/Hmq1uqVDa7X69OmDgoIC7XL06FHturVr1+L111/HW2+9he+//x5eXl647777tL+tRPrKy8vRr18/vPXWWwbXG5PTRx55BD/88AO2b9+O7du344cffkBMTExzDcGq3Sq/ADBu3Did93RKSorO+tjYWHz11VdISkrCvn37cPnyZUycOBG1tbWWDr9V2L17N+bPn4/9+/cjNTUVNTU1iIiIQHl5ubbPrXJYW1uLCRMmoLy8HPv27UNSUhK2bNmCp59+uqWGZTWMyS8AzJ49W+d9/N577+msN8vnhJDZDBo0SObMmaPT1qtXL3nhhRdaKKLWLS4uTvr162dwXV1dnXh5ecmaNWu0bVevXhWVSiXvvvtuc4XYqgGQr776SvtnY3Kam5srAGT//v3aPllZWQJAfv755+YLvhW4Mb8iIjNnzpSoqKgGt7l06ZLY29tLUlKStu3cuXNiY2Mj27dvt1isrVlRUZEAkN27d4uIcTlMSUkRGxsbOXfunLbPpk2bRKlUSklJSfMOwMrdmF8RkREjRsiiRYsa3MZcnxM8g2ImVVVVOHToECIiInTaIyIikJmZ2UJRtX4nTpyAt7c3/P39MW3aNJw+fRoAkJeXh8LCQp18K5VKjBgxgvk2kTE5zcrKgkqlQmhoqLbP4MGDoVKpmHcjZWRkoFOnTujRowdmz56NoqIi7bpDhw6hurpa5+/A29sbgYGBzG8DSkpKAABubm4AjMthVlYWAgMD4e3tre0zduxYVFZW4tChQ80YvfW7Mb8an3/+Odzd3dGnTx8888wzOmdZzfU5wUfrmcnvv/+O2tpaeHp66rR7enqisLCwhaJq3UJDQ7Fhwwb06NEDFy5cwCuvvILw8HDk5ORoc2oo32fOnGmJcFs9Y3JaWFiITp066W3bqVMnvs+NMH78eEyZMgW+vr7Iy8vDihUrMHr0aBw6dAhKpRKFhYVwcHCAq6urznb8HDFMRLB48WIMHToUgYGBAGBUDgsLC/Xe566urnBwcGCer2MovwAwffp0+Pv7w8vLC8eOHcOSJUuQnZ2N1NRUAOb7nGCBYmYKhULnzyKi10bGGT9+vPZ13759ERYWhoCAAHz66afaGwuZb/O7VU4N5Zd5N050dLT2dWBgIEJCQuDr64tt27Zh8uTJDW7H/Bq2YMEC/Pjjj9i3b98t+/J93HgN5Xf27Nna14GBgejevTtCQkJw+PBhBAUFATBPfnmJx0zc3d1ha2urVx0WFRXpVepkmvbt26Nv3744ceKEdjYP820+xuTUy8sLFy5c0Nv2t99+Y95N0LlzZ/j6+uLEiRMA6vNbVVWFixcv6vTj+1rfwoUL8a9//Qvp6eno0qWLtt2YHHp5eem9zy9evIjq6mrm+X8ayq8hQUFBsLe313kfm+NzggWKmTg4OCA4OFh7iksjNTUV4eHhLRRV21JZWYmffvoJnTt31p5evD7fVVVV2L17N/NtImNyGhYWhpKSEnz33XfaPgcOHEBJSQnzboLi4mKcPXsWnTt3BgAEBwfD3t5e5++goKAAx44dY37/R0SwYMECJCcnY9euXfD399dZb0wOw8LCcOzYMRQUFGj77NixA0qlEsHBwc0zECt1q/wakpOTg+rqau372GyfE8bfy0u3kpSUJPb29vLRRx9Jbm6uxMbGSvv27SU/P7+lQ2uVnn76acnIyJDTp0/L/v37ZeLEieLi4qLN55o1a0SlUklycrIcPXpUHn74YencubOUlpa2cOTWq6ysTI4cOSJHjhwRAPL666/LkSNH5MyZMyJiXE7HjRsn99xzj2RlZUlWVpb07dtXJk6c2FJDsio3y29ZWZk8/fTTkpmZKXl5eZKeni5hYWHi4+Ojk985c+ZIly5dJC0tTQ4fPiyjR4+Wfv36SU1NTQuOzHrMnTtXVCqVZGRkSEFBgXapqKjQ9rlVDmtqaiQwMFDGjBkjhw8flrS0NOnSpYssWLCgpYZlNW6V35MnT8pLL70k33//veTl5cm2bdukV69eMmDAAJ33qDk+J1igmNnbb78tvr6+4uDgIEFBQTpTs6hxoqOjpXPnzmJvby/e3t4yefJkycnJ0a6vq6uTuLg48fLyEqVSKcOHD5ejR4+2YMTWLz09XQDoLTNnzhQR43JaXFws06dPFxcXF3FxcZHp06fLxYsXW2A01udm+a2oqJCIiAjx8PAQe3t7ufPOO2XmzJmiVqt19nHlyhVZsGCBuLm5iZOTk0ycOFGvz+3MUH4BSGJioraPMTk8c+aMTJgwQZycnMTNzU0WLFggV69ebebRWJ9b5VetVsvw4cPFzc1NHBwcJCAgQJ566ikpLi7W2Y85PicU/wuIiIiIyGrwHhQiIiKyOixQiIiIyOqwQCEiIiKrwwKFiIiIrA4LFCIiIrI6LFCIiIjI6rBAISIiIqvDAoWIiIisDgsUojZu1qxZUCgUyM/Pb+lQzOKzzz5D//794ezsDIVCgfj4+JYOiYgsgAUKkZHy8/OhUCigUCgwceJEg30yMjKgUCgwZ86cZo7u9pCZmYmYmBhUVFRg/vz5iIuLw8iRI2+6jZ+fn/bvzdCSkZHRLLHX1NRAoVDg3nvvbZbjEbV2di0dAFFrtG3bNuzZswfDhw9v6VBuKykpKQCADRs2YPDgwUZvZ2tri+XLlxtc5+fnZ47QiMjMWKAQNZKfnx/UajWef/55ZGVltXQ4t5Xz588DALy8vBq1nZ2dHS8FEbUyvMRD1Eg9e/ZETEwM9u/fj+TkZKO28fPza/D/1EeOHAmFQqHTFh8fr738kJiYiL59+8LJyQn+/v548803AQAigr/97W/o1asXHB0d0aNHD2zcuLHBGGpra5GQkIC77roLjo6O6N69O9atW4e6ujqD/ffs2YPIyEi4u7tDqVSie/fuWL58OSoqKnT6aS5rxcfHIysrC2PHjkXHjh31xtSQzMxMTJgwAW5ubnB0dESvXr0QHx+vcxzNMRITEwEA/v7+2ks05nbhwgXExsYiICAASqUSHh4emDJlCnJzc/X67ty5E48++ih69uwJZ2dnODs7Y9CgQfjwww91+qWlpcHe3l67zfWXmD777DMAwPLly6FQKLBv3z6943z44Yc6fQHg5MmTUCgUeOKJJ5Cbm4sHH3wQd9xxBxQKBX799Vdtv1OnTuHxxx9H165d4eDgAG9vbzz22GM4e/as3nEOHjyIP/3pT+jatSuUSiU8PT0RHh6OdevWmZZMoibgGRQiE6xcuRJJSUlYunQpoqKiYGtra5HjvPHGG8jIyEBUVBRGjx6NLVu2YNGiRWjXrh2ys7PxxRdfYOLEiRg9ejSSkpIwY8YM+Pv7Y+jQoXr7io2Nxf79+zF16lQ4OjoiOTkZzz33HE6ePIn33ntPp++7776LefPmwdXVFZGRkfDw8MD333+PVatWIT09Henp6XBwcNDZJjMzE6tXr8aoUaPwl7/8BWq1+pbj27JlC6ZNmwYHBwdER0ejU6dOSEtLw0svvYQdO3YgPT0dSqUSfn5+iIuLw9atW5GdnY1FixahY8eOTUuuASdOnMDIkSNRUFCAcePGYfLkySgsLMSWLVuwfft2pKenIyQkRNs/ISEBZ86cweDBg+Hj44NLly7h22+/xezZs3HixAm8+uqrAIBu3bphxYoVePnll+Hv748ZM2Zo93HPPfc0KeZffvkFgwcPRr9+/TBr1iwUFxdri6GsrCyMGzcOV65cQWRkJAICApCXl4cNGzYgJSUFBw4cgK+vLwDg0KFDGDJkCOzt7TFp0iT4+fnhjz/+QE5ODj744AM8++yzTYqTqNGEiIySl5cnAGTs2LEiIrJ48WIBIO+99562T3p6ugCQJ598UmdbX19f8fX1NbjfESNGyI3/FOPi4gSAuLm5yalTp7TtarVaHBwcRKVSSY8ePaSoqEi77sCBAwJAJk2apLOvmTNnCgDx9PSUc+fOadvLysqkb9++AkD27Nmjbc/JyRE7OzsZMGCAFBcX6+wrISFBAMhrr72mN2YA8tFHHxkcoyGlpaXSsWNHUSqVkp2drW2vq6uTRx55RADIyy+/bHAseXl5Rh/H19dXbG1tJS4uTm/ZtGmTTt/Q0FCxt7eXnTt36rTn5uZK+/btZcCAATrtp0+f1jteVVWVjB49Wuzs7OTXX3/VtldXVwsAGTNmjME4ly1bJgBk7969eus++OADASAbN27Utp04cUKb95deeklvm8rKSunatauoVCo5evSozrqMjAyxsbGRBx54QNv21FNPCQDZtm2b3r5+//13gzETWRILFCIj3VigFBcXi0qlEm9vbykvLxcR8xco8fHxev1Hjx4tAOTTTz/VW9etWze942i+1FetWqXX/4svvhAA8vjjj2vbNF9Uhr4oa2trxcPDQ4KDg7VtmjHf+OV9Kxs2bBAAMnfuXL11arVa7OzsJCAgwOBYGlugaL7Ib1yioqK0/b777juDf3camrz89NNPtzzm5s2bBYB89tln2jZLFSg+Pj5SVVWlt80///lPASAJCQkGjzdp0iSxtbWVsrIynfHt2rXrluMjag68xENkIjc3Nzz//PNYunQp3njjDSxdutTsxxgwYIBeW+fOnQEA/fv3N7juwIEDBvc1bNiwBtt++OEHbdv+/fsBANu3b0daWpreNvb29vj555/12gcNGmTwuA05cuQIABicuIF6AQAABp1JREFUJty1a1cEBATg+PHjKCsrg4uLS6P2fSOlUomrV6/etI9m3OfPnzd4Q+0vv/wCAPj555/Rq1cvAEBpaSlee+01bN26FadPn0Z5ebnONpqbei2pf//+2ks619OMJzc31+B4ioqKUFtbi5MnT6J///6YMmUK/v73vyMyMhJTp07Ffffdh6FDh6Jr166WHgKRQSxQiJogNjYWb731FtauXYsnn3zS7Pvv0KGDXpudnd1N19XU1BjcV6dOnQy22djYoKSkRNv2xx9/AABWrVrVqFg9PT0b1b+0tPSm23l5eeH48eMoLS1tcoFiDM24v/nmG3zzzTcN9tMUIZWVlRg+fDiys7MRFBSEGTNmwM3NDXZ2djh9+jQ2btyIyspKi8fdUP4047nZjdPAtfEMHToUO3fuxJo1a/D5559rb0geOHAg1q1bhxEjRpgxaqJb4yweoiZwcnJCfHw8SkpKsHr16gb72djYNFg4XF8cWFJRUZHBtrq6OqhUKm2bpvApLS2F1F8GNrjcqLEzajTHuXDhgsH1mnZDhZglaI7zzjvv3HTc06dPBwAkJycjOzsbc+bMwaFDh7B+/Xq88soriI+PR0RERKOPb2NT/3Fs6H1ys/dIQ3nXjOfbb7+96XiGDBmi3WbUqFH4z3/+g4sXL2LXrl34v//7P/z444+4//7728yTiKn1YIFC1ESPPfYYevXqhbfffrvBmSuurq4oKirS+/IpLy/HiRMnmiNM7N27t8G26y8XhYaGArh2icBSNJevDD3J9dy5czh16hS6devWLGdPgGvjNvbZNqdOnQIATJo0SW+doVxrCpDa2lqD+3N1dQVQP/YbaS6HNUZjx3O9du3aYdSoUXj99dfx/PPPo6KiAjt37mz0foiaggUKURPZ2tpi9erVqKysxMqVKw32CQkJQXV1NT7//HNtm4hgyZIlevctWMqbb76pc0/E5cuXtfFeP+113rx5sLOzw8KFCw0+K+PSpUsmfWHeKCoqCiqVComJicjJydG2a/JSXV2NWbNmNfk4xgoPD0dISAg2btyIL7/8Um99XV0ddu/erf2zZnrujc8t2bVrFz7++GO97W1sbNCxY0edZ5RcTzN9ecOGDTrPptm3bx+SkpIaPZ7JkyejS5cuWLduHf773//qra+urtaJfc+ePSgrK9PrpzmT5eTk1OgYiJqC96AQmcGDDz6IsLCwBv9vdcGCBUhMTMQTTzyB1NRUeHh4YO/evbh06RL69euH7Oxsi8c4cOBA9OvXD9HR0VAqlUhOTkZ+fj5mz56t88j+wMBArF+/HnPnzkXPnj1x//33IyAgAKWlpTh9+jR2796NWbNm4d13321SPB06dMAHH3yAhx9+GKGhoYiOjoaHhwd27tyJgwcPYtCgQc3+7I2kpCSMGjUKU6ZMQVhYGIKCgqBUKqFWq5GVlYVLly7h8uXLAOoLrDvvvBMJCQn48ccfcffdd+P48eP497//jQcffBBbtmzR2//o0aORnJyMqVOnol+/frC1tUVkZCT69OmDoUOHYuDAgdixYweGDBmCoUOHIi8vD9988w0iIyOxdevWRo3F0dERX375JcaPH49hw4ZhzJgxCAwMhIjgzJkz2Lt3L7y8vHDs2DEAwNq1a5GRkYFRo0bB398fjo6OOHjwINLT09GzZ09ERUU1PcFEjdF8E4aIWrcbpxnfaM+ePdrpq4amqu7cuVNCQ0NFqVTKHXfcITExMVJYWHjTacbp6el6+7nZVFtD+9L0P3nypKxevVq6desmDg4OEhAQIK+++qrU1NQYHM93330n06ZNE29vb7G3txd3d3cJCgqSF154QWeqrWaacVxcnMH93MqePXtk/Pjx0rFjR3FwcJAePXrIihUr5PLly40ae0N8fX1FqVQa3b+4uFiWLVsmffr0EScnJ3F2dpbu3bvL9OnTZevWrTp9T548KZMnTxYPDw9p166dDBo0SL744gtJTU01+ByX8+fPy5QpU8Td3V0UCoXe1OELFy7I9OnTxdXVVZycnCQsLExSU1NvOs34+inihqjValm4cKHcddddolQqpUOHDnL33XfL7NmzdaYUp6SkSExMjPTo0UOcnZ3FxcVF+vTpIy+++KLe83CImoNCxMDdbkREREQtiPegEBERkdVhgUJERERWhwUKERERWR0WKERERGR1WKAQERGR1WGBQkRERFaHBQoRERFZHRYoREREZHVYoBAREZHVYYFCREREVocFChEREVkdFihERERkdf4faA6ClDRSXcEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f39225e160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124 features required for 0.99 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "fs.plot_feature_importances(threshold = 0.99, plot_n = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the feature importances are accessible in the `feature_importances` attribute of the `FeatureSelector`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>normalized_importance</th>\n",
       "      <th>cumulative_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>0.090544</td>\n",
       "      <td>0.090544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>259.4</td>\n",
       "      <td>0.082585</td>\n",
       "      <td>0.173130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>166.9</td>\n",
       "      <td>0.053136</td>\n",
       "      <td>0.226266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>142.7</td>\n",
       "      <td>0.045431</td>\n",
       "      <td>0.271697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.042152</td>\n",
       "      <td>0.313849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAYS_REGISTRATION</td>\n",
       "      <td>121.6</td>\n",
       "      <td>0.038714</td>\n",
       "      <td>0.352563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DAYS_ID_PUBLISH</td>\n",
       "      <td>117.2</td>\n",
       "      <td>0.037313</td>\n",
       "      <td>0.389876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DAYS_LAST_PHONE_CHANGE</td>\n",
       "      <td>114.9</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.426457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>114.2</td>\n",
       "      <td>0.036358</td>\n",
       "      <td>0.462814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AMT_ANNUITY</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.031519</td>\n",
       "      <td>0.494333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature  importance  normalized_importance  \\\n",
       "0            EXT_SOURCE_2       284.4               0.090544   \n",
       "1            EXT_SOURCE_3       259.4               0.082585   \n",
       "2            EXT_SOURCE_1       166.9               0.053136   \n",
       "3              DAYS_BIRTH       142.7               0.045431   \n",
       "4              SK_ID_CURR       132.4               0.042152   \n",
       "5       DAYS_REGISTRATION       121.6               0.038714   \n",
       "6         DAYS_ID_PUBLISH       117.2               0.037313   \n",
       "7  DAYS_LAST_PHONE_CHANGE       114.9               0.036581   \n",
       "8           DAYS_EMPLOYED       114.2               0.036358   \n",
       "9             AMT_ANNUITY        99.0               0.031519   \n",
       "\n",
       "   cumulative_importance  \n",
       "0               0.090544  \n",
       "1               0.173130  \n",
       "2               0.226266  \n",
       "3               0.271697  \n",
       "4               0.313849  \n",
       "5               0.352563  \n",
       "6               0.389876  \n",
       "7               0.426457  \n",
       "8               0.462814  \n",
       "9               0.494333  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Low Importance Features\n",
    "\n",
    "This method builds off the feature importances from the gradient boosting machine (`identify_zero_importance` must be run first) by finding the lowest importance features not needed to reach a specified cumulative total feature importance. For example, if we pass in 0.99, this will find the lowest important features that are not needed to reach 99% of the total feature importance. \n",
    "\n",
    "When using this method, we must have already run `identify_zero_importance` and need to pass in a `cumulative_importance` that accounts for that fraction of total feature importance.\n",
    "\n",
    "__Note of caution__: this method builds on the gradient boosting model features importances and again is non-deterministic. I advise running these two methods several times with varying parameters and testing each resulting set of features rather than picking one number and sticking to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "116 features that do not contribute to cumulative importance of 0.99.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_low_importance(cumulative_importance = 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ELEVATORS_MEDI',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'OCCUPATION_TYPE_Security staff',\n",
       " 'NAME_INCOME_TYPE_State servant',\n",
       " 'OCCUPATION_TYPE_Accountants']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_importance_features = fs.removal_ops['low_importance']\n",
    "low_importance_features[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing Features\n",
    "\n",
    "Once we have identified the features to remove, we have a number of ways to drop the features. We can access any of the feature lists in the `removal_ops` dictionary and remove the columns manually. We also can use the `remove` method, passing in the methods that identified the features we want to remove.\n",
    "\n",
    "__Be careful__ of the methods used for removing features! It's a good idea to inspect the features that will be removed before using the `remove` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 17 features\n"
     ]
    }
   ],
   "source": [
    "train_no_missing = fs.remove(methods = ['missing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 80 features\n"
     ]
    }
   ],
   "source": [
    "train_no_missing_zero = fs.remove(methods = ['missing', 'zero_importance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remove the features from all of the methods, pass in `method='all'`. Before we do this, we can check how many features will be removed using `check_removal`. This returns a list of all the features that have been idenfitied for removal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 143 features identified for removal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['APARTMENTS_MEDI',\n",
       " 'WALLSMATERIAL_MODE_Monolithic',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'ORGANIZATION_TYPE_Mobile',\n",
       " 'ORGANIZATION_TYPE_Trade: type 5',\n",
       " 'FLAG_DOCUMENT_5',\n",
       " 'FLAG_DOCUMENT_4',\n",
       " 'OCCUPATION_TYPE_High skill tech staff',\n",
       " 'OCCUPATION_TYPE_Realty agents',\n",
       " 'ORGANIZATION_TYPE_Trade: type 3',\n",
       " 'NAME_HOUSING_TYPE_With parents',\n",
       " 'ORGANIZATION_TYPE_Insurance',\n",
       " 'OBS_60_CNT_SOCIAL_CIRCLE',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'NAME_TYPE_SUITE_Children']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_to_remove = fs.check_removal()\n",
    "all_to_remove[10:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can remove all of the features idenfitied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 143 features\n"
     ]
    }
   ],
   "source": [
    "train_removed = fs.remove(methods = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling One-Hot Features\n",
    "\n",
    "If we look at the dataframe that is returned, we may notice several new columns that were not in the original data. These are created when the data is one-hot encoded for machine learning. To remove all the one-hot features, we can pass in `keep_one_hot = False` to the `remove` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 188 features\n"
     ]
    }
   ],
   "source": [
    "train_removed_all = fs.remove(methods = 'all', keep_one_hot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of Features 121\n",
      "Final Number of Features:  67\n"
     ]
    }
   ],
   "source": [
    "print('Original Number of Features', train.shape[1])\n",
    "print('Final Number of Features: ', train_removed_all.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative Option for Using all Methods\n",
    "\n",
    "If we don't want to run the identification methods one at a time, we can use `identify_all` to run all the methods in one call. For this function, we need to pass in a dictionary of parameters to use for each individual identification method. \n",
    "\n",
    "The following code accomplishes the above steps in one call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n",
      "4 features with a single unique value.\n",
      "\n",
      "21 features with a correlation greater than 0.98.\n",
      "\n",
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[19]\tvalid_0's auc: 0.763678\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[53]\tvalid_0's auc: 0.777007\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[89]\tvalid_0's auc: 0.710543\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[80]\tvalid_0's auc: 0.748537\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[223]\tvalid_0's auc: 0.733097\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[56]\tvalid_0's auc: 0.738958\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[86]\tvalid_0's auc: 0.761659\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[221]\tvalid_0's auc: 0.772005\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[126]\tvalid_0's auc: 0.75197\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[102]\tvalid_0's auc: 0.774734\n",
      "\n",
      "68 features with zero importance after one-hot encoding.\n",
      "\n",
      "126 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "113 features that do not contribute to cumulative importance of 0.99.\n",
      "\n",
      "139 total features out of 255 identified for removal after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)\n",
    "\n",
    "fs.identify_all(selection_params = {'missing_threshold': 0.6, 'correlation_threshold': 0.98, \n",
    "                                    'task': 'classification', 'eval_metric': 'auc', \n",
    "                                     'cumulative_importance': 0.99})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 139 features\n"
     ]
    }
   ],
   "source": [
    "train_removed_all_once = fs.remove(methods = 'all', keep_one_hot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a slight discrepancy between the number of features removed because the feature importances have changed. The number of features identified for removal by the `missing`, `single_unique`, and `collinear` will stay the same because they are deterministic, but the number of features from `zero_importance` and `low_importance` may vary due to training a model multiple times. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "This notebook showed how to use the `FeatureSelector` class for removing features from a dataset. There are a few important notes from this implementation:\n",
    "\n",
    "* Feature importances will change on multiple runs of the machine learning model\n",
    "* Decide whether or not to keep the extra features created from one-hot encoding\n",
    "* Try out several different values for the various parameters to decide which ones work best for a machine learning task\n",
    "* The output of missing, single unique, and collinear will stay the same for the identical parameters\n",
    "* Feature selection is a critical step of a machine learning workflow that may require several iterations to optimize\n",
    "\n",
    "I appreciate any comments, feedback, or help on this project. \n",
    "\n",
    "Best,\n",
    "\n",
    "Will"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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